{"id":311,"date":"2025-05-22T16:35:21","date_gmt":"2025-05-22T14:35:21","guid":{"rendered":"https:\/\/www.minesparis.psl.eu\/cbio\/?page_id=311"},"modified":"2025-08-14T17:58:47","modified_gmt":"2025-08-14T15:58:47","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.minesparis.psl.eu\/cbio\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n
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Pour consulter l\u2019ensemble des publications en dehors de ce site, rendez-vous sur les pages accessibles depuis les plateformes suivantes :<\/strong><\/p>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n

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PubMed <\/a><\/p>\n

Mots-cl\u00e9s : CBIO Mines Paris PSL<\/em><\/p>\n\t\t<\/div>\n\t\t

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2025<\/h2>\n
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  1. T. Bonte, L. Dubois, P. Gagna, R. Dibsy, A. Petrovi\u0107, T. Advedissian, M. Serres, F. Cuvelier, M. Crouigneau, N. Sassoon, N. Gupta-Rossi, S. Fr\u00e9mont, A. Echard, and T. Walter, \u201cCut-Detector: A Tool for Automated Temporal Analysis of Late Cytokinetic Events,\u201d bioRxiv<\/em>, June 2025, doi: 10.1101\/2025.06.06.658046<\/a>.<\/li>\n
  2. L. C. Gaspard-Boulinc, L. Gortana, T. Walter, E. Barillot, and F. M. G. Cavalli, \u201cCell-type deconvolution methods for spatial transcriptomics,\u201d Nature Reviews Genetics<\/em>, May 2025, doi: 10.1038\/s41576-025-00845-y<\/a>.<\/li>\n
  3. T. Bonte, O. Pourcelot, A. Safieddine, F. Slimani, F. Mueller, D. Weil, E. Bertrand, and T. Walter, \u201cA Deep Learning approach for time-consistent cell cycle phase prediction from microscopy data.\u201d bioRxiv<\/em>, 2025, doi:10.1101\/2025.05.16.654306<\/a>.<\/li>\n
  4. G. Balezo, R. Trullo, A. P. Planas, E. Decenciere, and T. Walter, \u201cMIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models.\u201d arXiv, May 2025, doi: 10.48550\/arXiv.2505.10294<\/a>.<\/li>\n
  5. T. Defard, A. Blondel, A. Coleon, G. Dias de Melo, T. Walter, and F. Mueller, \u201cRNA2seg: A generalist model for cell segmentation in image-based spatial transcriptomics,\u201d bioRxiv<\/em>, 2025, doi: 10.1101\/2025.03.03.641259<\/a>.<\/li>\n
  6. N. Captier, M. Lerousseau, F. Orlhac, N. Hovhannisyan-Baghdasarian, M. Luporsi, E. Woff, S. Lagha, P. Salamoun Feghali, C. Lonjou, C. Beaulaton, A. Zinovyev, H. Salmon, T. Walter, I. Buvat, N. Girard, and E. Barillot,\u00a0\u201cIntegration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer,\u201d\u00a0Nature Communications<\/em>, vol. 16, no. 1, p. 614, Jan. 2025, doi:\u00a010.1038\/s41467-025-55847-5<\/a>.<\/li>\n<\/ol>\n

    2024<\/h2>\n
      \n
    1. L. Chadoutaud, M. Lerousseau, D. Herrero-Saboya, J. Ostermaier, J. Fontugne, E. Barillot, and T. Walter: \u00ab\u00a0sCellST: A Multiple Instance Learning approach to predict single-cell gene expression from H&E images using spatial transcriptomics.\u201d, Nov. 2024, doi:10.1101\/2024.11.07.622225<\/a>.<\/li>\n
    2. N. Captier, M. Lerousseau, F. Orlhac, N. Hovhannisyan-Baghdasarian, M. Luporsi, E. Woff, S. Lagha, P. Salamoun Feghali, C. Lonjou, C. Beaulaton, H. Salmon, T. Walter, I. Buvat, N. Girard, and E. Barillot,\u00a0\u201cIntegration of clinical, pathological, radiological, and transcriptomic data improves the prediction of first-line immunotherapy outcome in metastatic non-small cell lung cancer.\u201d\u00a0Jun. 2024, doi:\u00a010.1101\/2024.06.27.24309583<\/a>.<\/li>\n
    3. A. Nouira and C.-A. Azencott. Sparse multitask group lasso for genome-wide association studies, BioRxiv<\/em>, 2024. doi: 10.1101\/2024.12.20.629593. [link<\/a>]<\/li>\n
    4. N. M. Mbaye, M. Danziger, A. Toussaint, E. Dumas, J. Gu\u00e9rin, A.-S. Hamy-Petit, F. Reyal, M. Rosen-Zvi, and C.-A. Azencott. Multimodal BEHRT: Transformers for Multimodal Electronic Health Records to predict breast cancer prognosis, medRxiv<\/em>, 2024. doi: 10.1101\/2024.09.18.24312984. [link<\/a>]<\/li>\n
    5. G. Gessain, A.-A. Anzali, M. Lerousseau, K. Mulder,\u00a0et al.<\/em>,\u00a0\u201cTrem2-expressing multinucleated giant macrophages are a biomarker of good prognosis in head and neck squamous cell carcinoma,\u201d\u00a0Cancer Discovery<\/em>, Sep. 2024, doi:\u00a010.1158\/2159-8290.CD-24-0018<\/a>.<\/li>\n
    6. M. Najm, L. Martignetti, M. Cornet,M. Kelly-Aubert, I. Sermet, L. Calzone, and V. Stoven. From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis., BMC Genomics, 25(1):892, 2024.\u00a0doi: 10.1101\/2023.11.15.567166 [link<\/a>]<\/li>\n
    7. G. Guichaoua, P. Pinel, B. Hoffmann, C.-A. Azencott, and V. Stoven. Drug-Target Interactions Prediction at scale: The Komet algorithm with the LCIdb dataset. . J. Chem. Inf. \u00a0Model, 64(18):6938-6956, 2024. doi: 10.1021\/acs.jcim.4c00422. [link<\/a>]<\/li>\n
    8. D. Zyss, A. Sharma, S. A. Ribeiro, C. E. Repellin, O. Lai, T. Walter, and A. Fehri,\u00a0\u201cContrastive learning for cell division detection and tracking in live cell imaging data.\u201d\u00a0Aug. 2024, doi:\u00a010.1101\/2024.08.16.608296<\/a>.<\/li>\n
    9. E. Dumas, B. Grandal Rejo, P. Gougis, S. Houzard, J. Ab\u00e9cassis, F. Jochum, B. Marande, A. Ballesta, E. Del Nery, T. Dubois, S. Alsafadi, B. Asselain, A. Latouche, M. Espie, E. Laas, F. Coussy, C. Bouchez, J.-Y. Pierga, C. Le Bihan-Benjamin, P.-J. Bousquet, J. Hotton, C.-A. Azencott, F. Reyal, and A.-S. Hamy.\u00a0Concomitant medication, comorbidity and survival in patients with breast cancer, Nature Communications<\/em>, 2024, doi:10.1038\/s41467-024-47002-3 [link<\/a>].<\/li>\n
    10. T. Defard, H. Laporte, M. Ayan, S. Juliette, S. Curras-Alonso, C. Weber, F. Massip, J.-A. Londo\u00f1o-Vallejo, C. Fouillade, F. Mueller, and T. Walter. A point cloud segmentation framework for image-based spatial transcriptomics, Communications Biology, <\/em>2024. doi:\u00a010.1038\/s42003-024-06480-3 [link<\/a>].<\/li>\n
    11. M. Michel, M. Heidary, A. Mechri, K. Da Silva, M. Gorse, V. Dixon, K. von Grafenstein, C. Bianchi, C. Hego, A. Rampanou, C. Lamy, M. Kamal, C. Le Tourneau, M. S\u00e9n\u00e9, I. Bi\u00e8che, C. Reyes, D. Gentien, M.-H. Stern, O. Lantz, L. Cabel, J.-Y. Pierga, F.-C. Bidard, C.-A. Azencott, and C. Proudhon. Non-invasive multi-cancer diagnosis using DNA hypomethylation of LINE-1 retrotransposons. Clinical Cancer Research<\/em>, 2024, p.OF1-OF17. doi:10.1158\/1078-0432.CCR-24-2669. [link<\/a>] [preprint<\/a>]<\/li>\n
    12. M. Sheinman, P. F. Arndt, F. Massip, Modeling the mosaic structure of bacterial genomes to infer their evolutionary history. PNAS<\/em>, March 2024. doi:\u00a010.1073\/pnas.2313367121 [link<\/a>]<\/li>\n
    13. X. Devos, J.-B. Fiche, M. Bardou, O. Messina, C. Houbron, J. Gurgo, M. Schaeffer, M. G\u00f6tz, T. Walter, F. Mueller, and M. Nollmann,\u00a0\u201cpyHiM: A new open-source, multi-platform software package for spatial genomics based on multiplexed\u00a0DNA-FISH\u00a0imaging,\u201d\u00a0Genome Biology<\/em>, vol. 25, no. 1, p. 47, Feb. 2024, doi:\u00a010.1186\/s13059-024-03178-x<\/a>.<\/li>\n
    14. C. A. Jahangir, D. B. Page, G. Broeckx, C. A. Gonzalez,\u00a0et al.<\/em>,\u00a0\u201cImage-based multiplex immune profiling of cancer tissues: Translational implications.\u00a0A\u00a0report of the\u00a0International Immuno-oncology\u00a0Biomarker Working Group\u00a0on\u00a0Breast Cancer,\u201d\u00a0The Journal of Pathology<\/em>, vol. 262, no. 3, pp. 271\u2013288, Mar. 2024, doi:\u00a010.1002\/path.6238<\/a>.<\/li>\n
    15. A. Beaufr\u00e8re, T. Lazard, R. Nicolle, G. Lubuela, J. Augustin, M. Albuquerque, B. Pichon, C. Pignolet, V. Priori, N. Th\u00e9ou-Anton, M. Lesurtel, M. Bouattour, K. Mondet, J. Cros, J. Calderaro, T. Walter, and V. Paradis,\u00a0\u201cSelf-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology,\u201d\u00a0Jan. 2024, doi:\u00a010.1101\/2024.01.15.575652<\/a>.<\/li>\n
    16. M. Najm, M. Cornet, L. Albergante, A. Zinovyev, I. Sermet-Gaudelus, V. Stoven, L. Calzone and L. Martignetti. Representation and quantification Of Module Activity from omics data with rROMA. NPJ Systems Biology and Applications<\/em>, 2024 Jan 19;10(1):8.<\/li>\n
    17. de Biase, M. S., Massip, F., Wei, T. T., Giorgi, F. M., Stark, R., Stone, A., Gladwell A., O’Reilly M., de Santiago I., Meyer K., Markowetz F., Ponder B. A. J., Rintoul R. C., Schwarz, R. F.\u00a0 Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk, Genome Medicine, <\/em>2024. doi:\u00a010.1186\/s13073-024-01317-4 . [link<\/a>]<\/li>\n<\/ol>\n

      2023<\/h2>\n
        \n
      1. T. Lazard, M. Lerousseau, S. Gardrat, A. Vincent-Salomon, M.-H. Stern, M. Rodrigues, E. Decenci\u00e8re, and T. Walter. Democratizing computational pathology: Optimized Whole Slide Image representations for The Cancer Genome Atlas, BioRXiv<\/em>, 2023. doi: 10.1101\/2023.12.04.569894 [link<\/a>].<\/li>\n
      2. T. Lazard, G. Bataillon, T. Walter, and A. Vincent Salomon. Cancer du sein – utilisation de l\u2019intelligence artificielle pour pr\u00e9dire le statut tumoral relatif \u00e0 la recombinaison homologue, Med Sci (Paris)<\/em>, vol. 39, no. 12, pp. 926\u2013928, 2023. doi: 10.1051\/medsci\/2023169 [link<\/a>].<\/li>\n
      3. A. Behdenna, M. Colange, J. Haziza, A. Gema, G. App\u00e9, C.-A. Azencott and A. Nordor. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics<\/em> 24, 459, 2023. doi: 10.1186\/s12859-023-05578-5 [link<\/a>]<\/li>\n
      4. S. Rorigues-Ferreira, M. Morin, G. Guichaoua, H. Moindjie, M. M. Haykal, O. Collier, V. Stoven and C. Nahmias. A Network of 17 Microtubule-Related Genes Highlights Functional Deregulations in Breast Cancer. Cancers (Basel),\u00a0202, 15(19):4870. doi: 10.3390\/cancers15194870 [link<\/a>].<\/li>\n
      5. M. Lubrano, Y. Bellahsen-Harrar, S. Berlemont, T. Walter, and C. Badoual. Diagnosis with Confidence: Deep\u00a0Learning for Reliable Classification\u00a0of\u00a0Squamous Lesions\u00a0of the Upper Aerodigestive\u00a0Tract, Histopathology<\/em>, p. his.15067, Oct. 2023, doi: 10.1111\/his.15067<\/a><\/li>\n
      6. D.B. Page, G. Broeckx, C. A. Jahangir, S. Verbandt, et al.<\/em>, Spatial analyses of immune cell infiltration in cancer: Current methods and future directions: A report of the International Immuno\u2010Oncology Biomarker Working Group on Breast Cancer, The Journal of Pathology<\/em>, vol. 260, no. 5, pp. 514\u2013532, Aug. 2023, doi: [link<\/a>].<\/li>\n
      7. J. Thagaard, G. Broeckx, D. B. Page, C. A. Jahangir, et al.<\/em>,Pitfalls in machine learning\u2010based assessment of tumor\u2010infiltrating lymphocytes in breast cancer: A report of the International Immuno\u2010Oncology Biomarker Working Group on Breast Cancer, The Journal of Pathology<\/em>, vol. 260, no. 5, pp. 498\u2013513, Aug. 2023, doi: 10.1002\/path.6155 [link<\/a>].<\/li>\n
      8. P. F. Arndt, F. Massip, M. Sheinman, An analytical derivation of the distribution of distances between heterozygous sites in diploid species to efficiently infer demographic history, in bioRXiv<\/em>, 2023. doi: 10.1101\/2023.09.20.558510 [link<\/a>]<\/li>\n
      9. A. Faiz, R. M. Mahbub, E. S. Boedijono, M. I. Tomassen, W. Kooistra, W. Timens, M. Nawijn, P. M. Hansbro, M. D. Johansen, S. D. Pouwels, I. H. Heijink, F. Massip, M. S. de Biase, R. F. Schwarz, I. M. Adcock, K. F. Chung, A. van der Does, P. S. Hiemstra, H. Goulaouic, H. Xing, R. Abdulai, E. de Rinaldis, D. Cunoosamy, S. Harel, D. Lederer, M. C. Nivens, P. A. Wark, H. A. M. Kerstjens, M. N. Hylkema, C. A. Brandsma, M. van den Berge, Cambridge Lung Cancer Early Detection Programme. IL-33 Expression Is Lower in Current Smokers at Both Transcriptomic and Protein Level, in. Am J Respir Crit Care Med.<\/em> 2023 Sep 14. doi: 10.1164\/rccm.202210-1881OC [link<\/a>]. Epub ahead of print. PMID: 37708400.<\/li>\n
      10. J. Poulet-Benedetti, C. Tonnerre-Doncarli, A.-L. Valton, M. Laurent, M. G\u00e9rard, N. Barinova, N. Parisis, F. Massip, F. Picard, M.-N. Prioleau. Dimeric G-quadruplex motifs-induced NFRs determine strong replication origins in vertebrates, in Nature communications, <\/em>Aug 2023. doi: 10.1038\/s41467-023-40441-4 [link<\/a>]<\/li>\n
      11. T. Lazard, M. Lerousseau, E. Decenci\u00e8re, and T. Walter, Giga-SSL: Self-supervised learning for gigapixel images, in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops<\/em>, Jun. 2023, pp. 4304\u20134313.<\/li>\n
      12. D. Zyss, S. A. Ribeiro, M. J. C. Ludlam, T. Walter, and A. Fehri. Cell Segmentation in Images Without Structural Fluorescent Labels, Biol. Imaging<\/em>, pp. 1\u201318, Jul. 2023, doi: 10.1017\/S2633903X23000168 [link<\/a>]<\/li>\n
      13. M. Lubrano, Y. Bellahsen-Harrar, R. Fick, C. Badoual, and T. Walter. Simple and E\ufb03cient Con\ufb01dence Score for Grading Whole Slide Images, in Proceedings of Machine Learning Research<\/em>, Jul. 2023, vol. 58, pp. 1\u201319.<\/li>\n
      14. Y. Bellahsen-Harrar, M. Lubrano, C. L\u00e9pine, A. Beaufr\u00e8re, C. Bocciarelli, A. Brunet, E. Decroix, F. N. El-Sissy, B. Fabiani, A. Morini, C. Tilmant, T. Walter, and C. Badoual. AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level, Pathology, preprint, Jul. 2023. doi: 10.1101\/2023.07.23.23292962 [link<\/a>]<\/li>\n
      15. C. Poulet, A. Debit, C. Josse, G. Jerusalem, C.-A. Azencott, V. Bours and K. Van Steen. Assessing random forest self-reproducibility for optimal short biomarker signature discovery, BioRxiv<\/em> 2023. [link<\/a>]<\/li>\n
      16. S. Curras-Alonso, J. Soulier, T. Defard, C. Weber, S. Heinrich, H. Laporte, S. Leboucher, S. Lameiras, M. Dutreix, V. Favaudon, F. Massip, T. Walter, F. Mueller, J.-A. Londo\u00f1o-Vallejo, and C. Fouillade. An interactive murine single-cell atlas of the lung responses to radiation injury, Nat Commun<\/em>, vol. 14, no. 1, p. 2445, Apr. 2023, doi: 10.1038\/s41467-023-38134-z [link<\/a>]<\/li>\n
      17. M. Lubrano, Y. Bellahsen-Harrar, R. Fick, C. Badoual, and T. Walter, A simple and efficient confidence score for grading whole slide images. arXiv, Mar. 08, 2023, [link<\/a>].<\/li>\n
      18. C. Le Priol, C.-A. Azencott, and X. Gidrol. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression, PLoS Computational Biology <\/em>19(3): e1010342 2023 [link<\/a>]<\/li>\n
      19. T. Bonte, M. Philbert, E. Coleno, E. Bertrand, A. Imbert, and T. Walter, Learning with minimal effort: Leveraging in silico labeling for cell and nucleus segmentation. arXiv, Jan. 10, 2023, [link<\/a>].<\/li>\n
      20. A. Imbert, F. Mueller, and T. Walter, PointFISH \u2013 learning point cloud representations for RNA localization patterns. arXiv, Feb. 21, 2023, [link<\/a>].<\/li>\n
      21. P. Pinel, G. Guichaoua, M. Najm, S. Labouille, N.\u00a0Drizard,\u00a0Y.\u00a0Gaston-Mathe\u0301,\u00a0B.\u00a0Hoffmann\u00a0and V.\u00a0Stoven. Exploring isofunctional molecules: design of a benchmark and evaluation of prediction performance. Molecular Informatics<\/em>, 2023. doi: 10.1002\/minf.202200216. [link<\/a>]<\/li>\n
      22. H. Climente-Gonz\u00e1lez, C.-A. Azencott, and M. Yamada. A network-guided protocol to discover susceptibility genes in genome-wide association studies using stability selection, STAR Protoc<\/em> 2023. [link<\/a>]<\/li>\n<\/ol>\n

        2022<\/h2>\n
          \n
        1. T. Lazard, G. Bataillon, P. Naylor, T. Popova, F.-C. Bidard, D. Stoppa-Lyonnet, M.-H. Stern, E. Decenci\u00e8re, T. Walter, and A. Vincent-Salomon; Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images. Cell Reports Medicine<\/em>, Dec. 2022, doi: 10.1016\/j.xcrm.2022.100872 [link<\/a>]<\/li>\n
        2. D. Drummond, J. Dana, L. Berteloot, E. K. Schneider-Futschik, F. Chedevergne, C. Bailly-Botuha, T. Nguyen-Khoa, M. Cornet, M. Le Bourgeois, D. Debray, M. Girard, I. Sermet-Gaudelus.\u00a0Lumacaftor-ivacaftor effects on cystic fibrosis-related liver involvement in adolescents with homozygous F508 del-CFTR.\u00a0 J Cyst Fibros<\/em>, 2022 21(2):212-219. doi: 10.1016\/j.jcf.2021.07.018. [link<\/a>]<\/li>\n
        3. E. Dumas, A.-S. Hamy, S. Houzard, E. Hernandez, A. Toussaint, J. Guerin, L. Chanas, V. de Castelbajac, M. Saint-Ghislain, B. Grandal, E. Daoud, F. Reyal, and C.-A. Azencott, EDEN : An Event DEtection Network for the annotation of Breast Cancer recurrences in administrative claims data, Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022<\/em> [preprint<\/a>].<\/li>\n
        4. A. Imbert, F. Mueller, and T. Walter, PointFISH: Learning Point Cloud Representations for RNA Localization Patterns, in Bioimage Conference (BIC) at the on Computer Vision (ECCV)<\/em>, Oct. 2022, p. 17.<\/li>\n
        5. T. Bonte, M. Philbert, E. Coleno, A. Imbert, and T. Walter, Learning with minimal effort: Leveraging in silico labeling for cell and nucleus segmentation, in Bioimage Computing (BIC) at the European Conference on Computer Vision (ECCV)<\/em>, Oct. 2022, p. 14.<\/li>\n
        6. C. Vesteghem, W. M. Szejniuk, R. F. Br\u00f8ndum, U. G. Falkmer, C.-A. Azencott, and M. B\u00f8gsted. Dynamic risk prediction of 30-day mortality of patients with advanced lung cancer: Comparing 5 machine learning approaches, JCO Clinical Cancer Informatics<\/em> no. 6 (2022) e2200054 [link<\/a>]<\/li>\n
        7. M. Lubrano, T. Lazard, G. Balezo, Y. Bellahsen-Harrar, C. Badoual, S. Berlemont, and T. Walter, Automatic grading of cervical biopsies by combining full and self-supervision, in Workshop on AI-enabled medical image analysis (AIMIA) at the European Conference on Computer Vision (ECCV)<\/em>, Oct. 2022, p. 14.<\/li>\n
        8. A. Safieddine, E. Coleno, F. Lionneton, A.-M. Traboulsi, S. Salloum, C.-H. Lecellier, T. Gostan, V. Georget, C. Hassen-Khodja, A. Imbert, F. Mueller, T. Walter, M. Peter, and E. Bertrand, HT-smFISH: A cost-effective and flexible workflow for high-throughput single-molecule RNA imaging, Nature Protocols<\/em>, Oct. 2022, doi: 10.1038\/s41596-022-00750-2<\/a>.<\/li>\n
        9. C. Le Priol, C.-A. Azencott, and X. Gidrol. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression, BioRxiv<\/em> 2022. [link<\/a>]<\/li>\n
        10. P. Naylor, T. Lazard, G. Bataillon, M. La\u00e9, A. Vincent-Salomon, A.-S. Hamy, F. Reyal, and T. Walter.\u00a0Prediction of\u00a0Treatment Response\u00a0in\u00a0Triple Negative Breast Cancer From Whole Slide Images.\u00a0Frontiers in Signal Processing<\/em>, vol. 2, p. 851809, Jun. 2022, doi:\u00a010.3389\/frsip.2022.851809<\/a>.<\/li>\n
        11. M. Cornet, G. Robin, F. Ciciriello, T. Bihouee, C. Marguet, V. Roy, M. Lebourgeois, F. Chedevergne, A.-S. Bonnel, M. Kelly, P. Reix, V. Lucidi, V. Stoven, and I. Sermet-Gaudelus.\u00a0Profiling the response to lumacaftor-ivacaftor in children with cystic between fibrosis and new insight from a French-Italian real-life cohort. Pediatr Pulmonol. <\/em>2022 Aug 22 [link<\/a>].<\/li>\n
        12. E. Daoud, A.-S. Hamy-Petit, E. Dumas, L. Delrieu, B. Grandal Rejo, C. Le Bihan-Benjamin, S. Houzard, P.-J. Bousquet, J. Hotton, A.-M. Savoye, C.\u00a0 Jouannaud, C.-A. Azencott, M. Lelarge, and F. Reyal. Disparities in accessibility to oncology care centers in France. BioRxiv<\/em> 2022, [preprint<\/a>].<\/li>\n
        13. B. Chevalier, N. Baatallah, M. Najm, S. Castanier, V. Jung, I. Pranke, A. Golec, V. Stoven, S. Marullo, F. Antigny, I. C. Guerrera, I. Sermet-Gaudelus, A. Edelman, and A. Hinzpeter.\u00a0Differential CFTR-Interactome Proximity Labeling Procedures Identify Enrichment in Multiple SLC Transporters. Int J Mol Sci.<\/em> 2022, 23(16):8937 [link<\/a>].<\/li>\n
        14. C.-A. Azencott. Introduction au Machine Learning (2\u00e8me \u00e9dition). Dunod InfoSup<\/em>, 2022. [link<\/a>]<\/li>\n
        15. L. Slim, H. de Foucauld, C. Chatelain, and C.-A. Azencott. A systematic analysis of gene-gene interaction in multiple sclerosis, BMC Medical Genomics<\/em> 15:100 (2022) [link<\/a>].<\/li>\n
        16. E. Dumas, L. Laot, F. Coussy, B. Grandal Rejo, E. Daoud, E. Laas, A. Kassara, A. Majdling, R. Kabirian, F. Jochum, P. Gougis, S. Michel, S. Houzard, C. Le Bihan-Benjamin, P.-J. Bousquet, J. Hotton, C.-A. Azencott, F. Reyal, and A.-S. Hamy, The French Early Breast Cancer Cohort (FRESH): a resource for breast cancer research and evaluations of oncology practices based on the French National Healthcare System Database (SNDS), Cancers<\/em> 2022 14(11), 2671. [preprint<\/a>] [link<\/a>]<\/li>\n
        17. D. Duroux, H. Climente-Gonz\u00e1lez, C.-A. Azencott, and K. Van Steen. Interpretable network-guided epistasis detection, GigaScience<\/em> 11:giab093, 2022. [link<\/a>].<\/li>\n
        18. M. Lubrano di Scandalea, T. Lazard, G. Balezo, Y. Bellahsen-Harrar, C. Badoual, S. Berlemont, and T. Walter, Automatic grading of cervical biopsies by combining full and self-supervision, BioRxiv<\/em>,\u00a0Jan. 2022, [link]<\/a><\/li>\n
        19. A. Nouira, C.-A. Azencott. Multitask group Lasso for Genome Wide Association Studies in diverse populations, Pacific Symposium on Biocomputing<\/em> 27:163-174, 2022. [preprint<\/a>] [link<\/a>]<\/li>\n
        20. L. Slim, C. Chatelain, and C.-A. Azencott. Nonlinear post-selection inference for genome-wide association studies, Pacific Symposium on Biocomputing<\/em> 27:349-360, 2022. [preprint<\/a>] [link<\/a>]<\/li>\n
        21. A. Jouinot, J.\u00a0Lippert, M. Sibony, L. Jeanpierre, D. De Murat, R. Armignacco, A. Septier, K. Perlemoine, F. Letourneur, B. Izac, B. Ragazzon, K. Leroy, E. Pasmant, M.O. North, S. Gaujoux, B. Dousset, L. Groussin, R. Libe, B. Terris, M. Fassnacht, C.L. Ronchi, J. Bertherat, G. Assi\u00e9.\u00a0Transcriptome in paraffin samples for the diagnosis and prognosis of adrenocortical carcinoma, Eur J Endocrinol <\/em>2022 Mar 1:EJE-21-1228. [link<\/a>]<\/li>\n
        22. Hananeh Aliee, Florian Massip, Cancan Qi, Maria Stella de Biase, Jos van Nijnatten, Elin TG Kersten, Nazanin Z Kermani, Basil Khuder, Judith M Vonk, Roel CH Vermeulen, U\u2010BIOPRED study group, Cambridge Lung Cancer Early Detection Programme, INER\u2010Ciencias Mexican Lung Program, Margaret Neighbors, Gaik W Tew, Michele A Grimbaldeston, Nick HT ten Hacken, Sile Hu, Yike Guo, Xiaoyu Zhang, Kai Sun, Pieter S Hiemstra, Bruce A Ponder, Mika J M\u00e4kel\u00e4, Kristiina Malmstr\u00f6m, Robert C Rintoul, Paul A Reyfman, Fabian J Theis, Corry\u2010Anke Brandsma, Ian M Adcock, Wim Timens, Cheng\u2010Jian Xu, Maarten van den Berge, Roland F Schwarz, Gerard H Koppelman, MC Nawijn, Alen Faiz. Determinants of expression of SARS\u2010CoV\u20102 entry\u2010related genes in upper and lower airways, Allergy,<\/em> 2022 Feb. https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.1111\/all.15152<\/a><\/li>\n
        23. A. Vaczlavik, L. Bouys, F. Violon, G. Giannone, A. Jouinot, R. Armignacco, I.P. Cavalcante, A. Berthon, E. Letouz\u00e9, P. Vaduva, M. Barat, F. Bonnet, K. Perlemoine, C. Ribes, M. Sibony, M.O. North, S. Espiard, P. Emy, M. Haissaguerre, I. Tauveron, L. Guignat, L. Groussin, B. Dousset, M. Reincke, M.C. Fragoso, C.A. Stratakis, E. Pasmant, R. Lib\u00e9, G. Assi\u00e9, B. Ragazzon, J. Bertherat.\u00a0\u00a0KDM1A inactivation causes hereditary food-dependent Cushing syndrome,\u00a0Genet Med<\/em> 2022 Feb;24(2):374-383. [link<\/a>]<\/li>\n<\/ol>\n

          2021<\/h2>\n
            \n
          1. X. Pichon, K. Moissoglu, E. Coleno, T. Wang, A. Imbert, M. Peter, R. Chouaib, T. Walter, F. Mueller, K. Zibara, E. Bertrand, and S. Mili. The kinesin KIF1C transports APC-dependent mRNAs to cell protrusions, RNA<\/em>\u00a027(12):1528-1544, 2021.\u00a0[link<\/a>]<\/li>\n
          2. V. Mallet, C. Oliver, J. Broadbent, W.L. Hamilton, J. Waldisp\u00fchl. RNAglib: A Python Package for RNA 2.5 D Graphs Bioinformatics application notes<\/em>, Dec 2021. [link<\/a>]<\/li>\n
          3. C. Oliver, V. Mallet, P. Philippopoulos, W.L. Hamilton, J. Waldisp\u00fchl. VeRNAl: A Tool for Mining Fuzzy Network Motifs in RNA Bioinformatics <\/em>, Nov 2021. [link<\/a>]<\/li>\n
          4. V. Mallet, L. Checa Ruano, A. Moine Franel, M. Nilges, K. Druart, G. Bouvier, O. Sperandio. InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions Bioinformatics <\/em>, Nov 2021. [link<\/a>]<\/li>\n
          5. V. Mallet, J.P. Vert. Reverse-Complement Equivariant Networks for DNA Sequences NeurIPS proceedings <\/em>, Dec 2021. [link<\/a>]<\/li>\n
          6. B. Baussart, C. Villa, A.\u00a0Jouinot, M.L. Raffin-Sanson, L. Foubert, L. Cazabat, M. Bernier, F. Bonnet, A. Dohan, J. Bertherat, G. Assi\u00e9, S. Gaillard.\u00a0Pituitary surgery as alternative to dopamine agonists treatment for microprolactinomas: a cohort study,\u00a0Eur J Endocrinol <\/em>2021 Oct 21;185(6):783-791. [link<\/a>]<\/li>\n
          7. T. Lazard, G. Bataillon, P. Naylor, T. Popova, F.-C. Bidard, D. Stoppa-Lyonnet, M.-H. Stern, E. Decenci\u00e8re, T. Walter, and A. V. Salomon. Deep learning identifies new morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images. bioRxiv<\/em>, Sep. 2021. [link<\/a>]<\/li>\n
          8. J. Abecassis, F. Reyal and J.-P. Vert. Clonesig can jointly infer intra-tumor heterogeneity and mutational signature activity in tumor bulk sequencing data. Nature Communications<\/em>, 12:5352, 2021. [link<\/a>]<\/li>\n
          9. A. Imbert, W. Ouyang, A. Safieddine, E. Coleno, C. Zimmer, E. Bertrand, T. Walter, and F. Mueller. FISH-quant v2: A scalable and modular analysis tool for smFISH image analysis. bioRxiv<\/em>, Jul. 2021, [link<\/a>]<\/li>\n
          10. E. Daoud, A.-S. Hamy-Petit, E. Dumas, L. Delrieu, B. Grandal Rejo, C. Le Bihan-Benjamin, S. Houzard, P.-J. Bousquet, J. Hotton, A.-M. Savoye, C. Jouannaud, C.-A. Azencott, M. Lelarge, F. Reyal. Disparities in accessibility to oncology care centers in France, medRxiv<\/em>, 2021. [link<\/a>]<\/li>\n
          11. Y. Jiao, F. Lesueur, C.-A. Azencott, M. Laurent, N. Mebirouk, L. Laborde, J. Beauvallet, M.-G. Dondon, S. Eon-Marchais, A. Laug\u00e9, GEMO Study Collaborators, GENEPSO Study Collaborators, C. Nogu\u00e8s, N. Andrieu, D. Stoppa-Lyonnet, and S. M. Caputo. A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers, BMC Medical Research Methodology<\/em>, 2021.[link<\/a>]<\/li>\n
          12. M. Najm, C.-A. Azencott, B. Playe, and V. Stoven. Drug Target Identification with Machine Learning: How to Choose Negative Examples, International Journal of Molecular Sciences<\/em>, 2021. [link<\/a>]<\/li>\n
          13. V. Goepp, J.-C. Thalabard, G. Nuel, and O. Bouaziz. Regularized Bidimensional Estimation of the Hazard Rate, The International Journal of Biostatistics<\/em>, 2021. [link<\/a>]<\/li>\n
          14. A. Behdenna, J. Haziza, C.-A. Azencott, and A. Nordor. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods, bioRxiv<\/em>, 2021. [link<\/a>]<\/li>\n
          15. H. Climente-Gonz\u00e1lez, C. Lonjou, F. Lesueur, GENESIS Study collaborators, D. Stoppa-Lyonnet, N. Andrieu, and C.-A. Azencott. Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer, PLoS Comput Biol<\/em> 17(3): e1008819, 2021. [link<\/a>]<\/li>\n
          16. A. Safieddine, E. Coleno, S. Salloum, A. Imbert, A.-M. Traboulsi, O. S. Kwon, F. Lionneton, V. Georget, M.-C. Robert, T. Gostan, C.-H. Lecellier, R. Chouaib, X. Pichon, H. Le Hir, K. Zibara, F. Mueller, T. Walter, M. Peter, and E. Bertrand. A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport, Nature Communications<\/em>, vol. 12, no. 1, pp. 1352, 2021. [link<\/a>]<\/li>\n
          17. S. Curras-Alonso, J. Soulier, T. Walter, F. Mueller, A. Londo\u00f1o-Vallejo, and C. Fouillade. Spatial transcriptomics for respiratory research and medicine, Eur Respir J<\/em>, p. 2004314, 2021. [link<\/a>]<\/li>\n
          18. F. Raimundo, L. Papaxanthos, C. Vallot and J.-P. Vert. Machine learning for single cell genomics data analysis. Current Opinion in Systems Biology<\/em>, 25:64-71, 2021. [link<\/a>]<\/li>\n
          19. L. Delrieu, L. Bouaoun, D. E. Fatouhi, E. Dumas, A.-D. Bouhnik, H. Noelle, E. Jacquet, A.-S. Hamy, F. Coussy, F. Reyal, P.-E. Heudel, M.-K. Bendiane, B. Fournier, M. Michallet, B. Fervers, G. Fagherazzi, O. P\u00e9rol. Patterns of Sequelae in Women with a History of Localized Breast Cancer: Results from the French VICAN Survey. Cancers<\/em>. 2021; 13(5):1161. [link<\/a>]<\/li>\n
          20. N. Varoquaux, W. S. Noble and J.-P. Vert. Inference of genome 3D architecture by modeling overdispersion of Hi-C data. BioRxiv<\/em> 429864, 2021. [link<\/a>]<\/li>\n
          21. H. Climente-Gonz\u00e1lez and C.-A. Azencott. martini: an R package for genome-wide association studies using SNP networks, bioRxiv<\/em>, 2021. [link<\/a>]<\/li>\n
          22. V. Tozzo, C.-A. Azencott, S. Fiorini, E. Fava, A. Trucco, and A. Barla. Where do we stand in regularization for life science studies? Journal of Computational Biology<\/em>, 2021. [link<\/a>]<\/li>\n<\/ol>\n

            2020<\/h2>\n
              \n
            1. J. Boitreaud, V. Mallet, C. Oliver, J. Waldispuhl. OptiMol: Optimization of binding affinities in chemical space for drug discovery JCIM <\/em>, 2020. [link<\/a>]<\/li>\n
            2. C. Oliver, V. Mallet, R.S. Gendron, V. Reinharz, W.L. Hamilton, N. Moitessier, J. Waldispuhl. Augmented base pairing networks encode RNA-small molecule binding preferences. NAR <\/em>, 2020. [link<\/a>]<\/li>\n
            3. V. Mallet, M. Nilges, G. Bouvier. Quicksom: Self-Organizing maps on GPUs for clustering of molecular dynamics trajectories Bioinformatics Application Notes<\/em>, 2020. [link<\/a>]<\/li>\n
            4. L. Slim, C. Chatelain, C.-A. Azencott, and J.-P. Vert. Novel methods for epistasis detection in genome-wide association studies. PLoS ONE<\/em>, 2020. [link<\/a>]<\/li>\n
            5. S. Gauthier, I. Pranke, V. Jung, L. Martignetti, V.Stoven, T. Nguyen-Khoa, M. Semeraro, A. Hinzpeter, A. Edelman, C. Guerrera, and I. Sermet-Gaudelus. Urinary exosomes of patients with Cystic Fibrosis unravel CFTR related renal disease, International Journal for Molecular Sciences<\/em>, 10;21(18):6625 2020. [link<\/a>]<\/li>\n
            6. L. Slim, H. de Foucauld, C. Chatelain, and C.-A. Azencott. A systematic analysis of gene-gene interaction in multiple sclerosis, bioRxiv<\/em>, 2020. [link<\/a>]<\/li>\n
            7. M. Balluet, F. Sizaire, T. Walter, J. Pont, B. Giroux, O. Bouchareb, M. Tramier, and J. Pecreaux. Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy. bioRxiv<\/em>, 2020. [link<\/a>]<\/li>\n
            8. B. Playe and V. Stoven. Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity, Journal of chemoinformatics<\/em>, 12, 11, 2020. [link<\/a>].<\/li>\n
            9. R. Chouaib, A. Safieddine, X. Pichon, A. Imbert, O. Sung Kwon, A. Samacoits, A.-M. Traboulsi, M.-C. Robert, N. Tsanov, E. Coleno, I. Poser, C. Zimmer, A. Hyman, H. Le Hir, K. Zibara, M. Peter, F. Mueller, T. Walter, and E. Bertrand. A dual protein-mRNA localization screen reveals compartmentalized translation and widespread co-translational RNA targeting. Developmental Cell<\/em> 54\u00a0(6), 773-791, 2020.\u00a0[link<\/a>]<\/li>\n
            10. P.-C. Aubin-Frankowski and J.-P. Vert. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Bioinformatics<\/em> 36(18):4774-4780, 2020. [link<\/a>]<\/li>\n
            11. J. Boyd, Z. Gouveia, F. Perez and T. Walter, Experimentally-generated ground truth for detecting cell types in an image-based immunotherapy screen, IEEE 17th International Symposium on Biomedical Imaging (ISBI)<\/em>, Iowa City, IA, USA, pp. 886-890, 2020. [link<\/a>]<\/li>\n
            12. M.-M. Aynaud, O. Mirabeau, N. Gruel, S. Grosset\u00eate, V. Boeva, S. Durand, D. Surdez, O. Saulnier, S. Za\u00efdi, S. Gribkova, U. Kairov, V. Raynal, F. Tirode, P. Gr\u00fcnewald, M. Bohec, S. Baulande, I. Janoueix-Lerosey, J.-P. Vert, E. Barillot, O. Delattre and A. Zinovyev. Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single cell resolution. Cell Reports<\/em>, 30(6):1767-1779.E6, 2020. [link<\/a>]<\/li>\n
            13. K. B. Cook, B. H. Hristov, K. G. Le Roch, J.-P. Vert and W. S. Noble. Measuring significant changes in chromatin conformation with ACCOST. Nucleic Acids Research<\/em>, 48(5):2303-2311, 2020. [link<\/a>]<\/li>\n<\/ol>\n

              2019<\/h2>\n
                \n
              1. J. Abecassis, A.-S. Hamy, C. Laurent, B. Sadacca, H. Bonsang-Kitzis, F. Reyal and J.-P. Vert. Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data.\u00a0PLoS ONE<\/em>, 14(11):e0224143, 2019. [link<\/a>]<\/li>\n
              2. O. Collier, V. Stoven, J.-P. Vert. A single- and multi-task machine learning algorithm for the prediction of cancer driver genes. PLoS Computational Biology<\/em> 15(9):e1007381, 2019. [link<\/a>]<\/li>\n
              3. J. Boyd, A. Pinheiro, E. Del Nery, F. Reyal, T. Walter. Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen. Bioinformatics.<\/em> [link<\/a>]<\/li>\n
              4. A. G. Cauer, G. Yardimci, J.-P. Vert, N. Varoquaux and W. S. Noble. Inferring diploid 3D chromatin structures from Hi-C data.\u00a0In K. T. Huber and D. Gusfield (Eds),\u00a0Proceedings of the 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)<\/em>, Leibniz International Proceedings in Informatics (LIPIcs) 143:11, 2019. [link<\/a>]<\/li>\n
              5. J. Liu, Y. Huang, R. Singh, J.-P. Vert and W. S. Noble. Jointly embedding multiple single-cell omics measurements.\u00a0In K. T. Huber and D. Gusfield (Eds),\u00a0Proceedings of the 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)<\/em>, Leibniz International Proceedings in Informatics (LIPIcs) 143:10, 2019. [link<\/a>]<\/li>\n
              6. H. Climente-Gonz\u00e1lez, C.-A. Azencott, S. Kaski, M. Yamada. Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data. Bioinformatics<\/em>, 2019, 35(14) i427\u2013i435, (ISMB\/ECCB Proceedings). [link<\/a>]<\/li>\n
              7. L. Slim, C. Chatelain, C.-A. Azencott, J.-P. Vert. kernelPSI: a post-selection inference framework for nonlinear variable selection, Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML)<\/em>, 2019 97:5857\u20145865. [link<\/a>]<\/li>\n
              8. R. Menegaux and J.-P. Vert. Continuous embeddings of DNA sequencing reads, and application to metagenomics.\u00a0Journal of Computational Biology<\/em>,\u00a026(6):509-518, 2019.[link<\/a>]\u00a0 M. Durand, T. Walter, T. Pirnay, T. Naessens, P. Gueguen, C. Goudot, S. Lameiras, Q. Chang, N. Talaei, O. Ornatsky, T. Vassilevskaia, S. Baulande, S. Amigorena, and E. Segura. Human lymphoid organ cDC2 and macrophages play complementary roles in T follicular helper responses.\u00a0Journal of experimental medicine<\/em>, Epub: May 2019. [link<\/a>]<\/li>\n
              9. P. Naylor, J. Boyd, M.\u00a0Lae, F. Reyal, T. Walter. Predicting Residual Cancer Burden in a Triple Negative Breast Cancer cohort.\u00a0Proceedings of the 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019).<\/em> Venice, Italy. April 2019. [link<\/a>]<\/li>\n
              10. R. Dubois, A. Imbert, A. Samacoits, M. Peter, E. Bertrand, F. M\u00fcller, T. Walter. A deep learning approach to identify mRNA localization patterns.\u00a0Proceedings of the 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019).\u00a0<\/em>Venice, Italy. April 2019. [link<\/a>]<\/li>\n<\/ol>\n

                2018<\/h2>\n
                  \n
                1. E. Pauwels, F. Bach and J.-P. Vert. Relating leverage scores and density using regularized Christoffel functions. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi and R. Garnett (Eds.), Advances in Neural Information Processing Systems (NeurIPS) 31<\/em>, 1670-79, 2018. [link<\/a>]<\/li>\n
                2. A. Samacoits, R. Chouaib, A. Safieddine, A.-M. Traboulsi, W. Ouyang, C. Zimmer, M. Peter, E. Bertrand, T. Walter and F. Mueller. A computational framework to study sub-cellular RNA localization. Nature Communications<\/em>, 9<\/em>(1), 4584, 2018. [link<\/a>]<\/li>\n
                3. T. Baladi, J. Aziz, F. Dufour, V. Abet, V. Stoven, F. Radvanyi, F. Poyer, T.D. Wu, J.-L. Guerquin-Kern, I. Bernard-Pierrot, S. M. Garrido, S. Piguel. Design, synthesis, biological evaluation and cellular imaging of imidazo[4,5-b]pyridine derivatives as potent and selective TAM inhibitors. Bioorg Med Chem,<\/em> pii S0968-0896(18)31380-4, 2018.\u00a0 B. Playe, C.-A. Azencott and V. Stoven. Efficient multi-task chemogenomics for drug specificity prediction. PLoS ONE<\/em> 13(10), 2018. [link<\/a>]<\/li>\n
                4. C.-A. Azencott. Introduction au Machine Learning. Dunod InfoSup<\/em>, 2018. [link<\/a>]<\/li>\n
                5. N. Servant, N. Varoquaux, E. Heard, J.-P. Vert and E. Barillot. Effective normalization for copy number variation in Hi-C data. BMC Bioinformatics<\/em>, 19:313, 2018. [link<\/a>]<\/li>\n
                6. P. Naylor, M. La\u00eb, F. Reyal and T. Walter. Segmentation of Nuclei in Histopathology Images by deep regression of the distance map. IEEE Transactions on Medical Imaging<\/em>, 2018. <\/em>[link<\/a>]<\/li>\n
                7. C.-A. Azencott. Machine learning and genomics: precision medicine versus patient privacy. Philosophical Transactions of the Royal Society A<\/em>, 2018. [link<\/a>]<\/li>\n
                8. [arxiv pre-print<\/a>]<\/li>\n
                9. Y. Jiao and J.-P. Vert. The weighted Kendall and high-order kernels for permutations. In J. Dy and A. Krause (Eds.), Proceedings of the 35th International Conference on Machine Learning<\/em>, PMLR 80:2314-2322, 2018. [link<\/a>]<\/li>\n
                10. M. Le Morvan and J.-P. Vert. WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models. In J. Dy and A. Krause (Eds.), Proceedings of the 35th International Conference on Machine Learning<\/em>, PMLR 80:3635-3644, 2018. [link<\/a>]<\/li>\n
                11. J. Boyd, A. Pinhiero, E. Del Nery, F. Reyal and T. Walter. Analysing Double-Strand Breaks in Cultured Cells for Drug Screening Applications by Causal Inference. Proceedings of the 15th IEEE International Symposium on Biomedical Imaging (ISBI)<\/em>. Washington D.C., United State of America. April, 2018. [link<\/a>]<\/li>\n
                12. K. Vervier, P. Mah\u00e9 and J.-P. Vert. MetaVW: Large-scale machine learning for metagenomics sequence classification. In Mamitsuka H. (eds), Data Mining for Systems Biology. Methods in Molecular Biology<\/em>, vol. 1807:9-20, Humana Press, New York, NY, 2018. [link<\/a>]<\/li>\n
                13. Y. Jiao and J.-P. Vert. The Kendall and Mallows kernels for permutations. IEEE Transactions on Pattern Analysis and Machine Intelligence<\/em>, 40(7):1755-1769, 2018. [link<\/a>]<\/li>\n
                14. E. M. Bunnik, K. B. Cook, N. Varoquaux, G. Batugedara, J. Prudhomme, A. Cort, L. Shi, C. Andolina, L. S. Ross, D. Brady, D. A. Fidock, F. Nosten, R. Tewari, P. Sinnis, F. Ay, J.-P. Vert, W. Noble and K. G. Le Roch. Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages. Nature Communications<\/em>, 9(1):1910, 2018. [link<\/a>]<\/li>\n
                15. K. Van den Berg, F. Perraudeau, C. Soneson, M. I. Love, D. Risso, J.-P. Vert, M. D. Robinson, S. Dudoit and L. Clement. Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biology<\/em>, 19:24, 2018. [link<\/a>]<\/li>\n
                16. P. Ruan, M. Hayashida, T. Akutsu and J.-P. Vert. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel. BMC Bioinformatics<\/em>, 19(Suppl 1):39, 2018. [link<\/a>]<\/li>\n
                17. D. Risso, F. Perraudeau, S. Gribkova, S. Dudoit and J.-P. Vert. ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data. Nature Communications<\/em>, 9(1):284, 2018. [link<\/a>]<\/li>\n<\/ol>\n

                  2017<\/h2>\n
                    \n
                  1. J.-P. Vert. Quand les algorithmes font parler l’ADN.\u00a0La Recherche<\/em>, 529:48-52, 2017. P. Naylor, M. La\u00e9, F. Reyal and T. Walter. Nuclei segmentation in histopathology images using deep neural networks. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)<\/em>, Melbourne, VIC, p.933-936, 2017. [link<\/a>]<\/li>\n
                  2. J.-L. Plouhinec, S. Medina-Ruiz, C. Borday, E. Bernard, J.-P. Vert, M. B. Eisen, R. M. Harland and A. H. Monsoro-Burq. A molecular atlas of the developing ectoderm defines neural, neural crest, placode and non-neural progenitor identity in vertebrates.\u00a0PLoS Biology<\/em>, 15(10): e2004045, 2017. [link<\/a>]<\/li>\n
                  3. E. Bernard, Y. Jiao, E. Scornet, V. Stoven, T. Walter and J.-P. Vert. Kernel multitask regression for toxicogenetics.\u00a0Molecular Informatics<\/em>, 36(10), 2017. [link<\/a>]<\/li>\n
                  4. C.-A. Azencott, T. Aittokallio, S. Roy, T. Norman, S. Friend, G. Stolovitzky, A. Goldenberg, and DREAM Idea Challenge Consortium. The inconvenience of data of convenience: computational research beyond post-mortem analyses. Nature Methods<\/em> 14(10):937-938, 2017. [link<\/a>]<\/li>\n
                  5. H. Climente-Gonz\u00e1lez, E. Porta-Pardo, A. Godzik and E. Eyras. The Functional Impact of Alternative Splicing in Cancer. Cell Reports<\/em>, 20<\/em>(9), 2215\u20132226, 2017. [link<\/a>]<\/li>\n
                  6. M. Le Morvan, A. Zinovyev and J.-P. Vert.\u00a0NetNorM: capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis.\u00a0PLoS Computational Biology,\u00a0<\/em>13(6):e1005573, 2017.\u00a0[link<\/a>]<\/li>\n
                  7. H. P. T\u00f6r\u00f6k, V. Bellon, A. Konrad, M. Lacher, L. Tonenchi, M. Siebeck, S. Brand, E. N. De Toni.\u00a0Functional\u00a0Toll-Like Receptor (TLR)2\u00a0polymorphisms in the susceptibility to inflammatory bowel disease.\u00a0PLoS ONE<\/em>, 12(4):e0175180, 2017. [link<\/a>]<\/li>\n<\/ol>\n

                    2016<\/h2>\n
                      \n
                    1. A.-S. Hamy , H. Bonsang-Kitzis , M. Lae, M. Moarii, B. Sadacca, A. Pinheiro, M. Galliot, J. Abecassis, C. Laurent and\u00a0F. Reyal.\u00a0A Stromal Immune Module Correlated with the Response to Neoadjuvant Chemotherapy, Prognosis and Lymphocyte Infiltration in HER2-Positive Breast Carcinoma Is Inversely Correlated with Hormonal Pathways.\u00a0PLoS ONE<\/em>\u00a011(12):e0167397, 2016. [link<\/a>]<\/li>\n
                    2. N. Tsanov, A. Samacoits, R. Chouaib, A.-M. Traboulsi, T. Gostan, C. Weber, C. Zimmer, K. Zibara, T. Walter, M. Peter, E. Bertrand and\u00a0F. Mueller. smiFISH and FISH-quant – a flexible single RNA detection approach with super-resolution capability.\u00a0Nucleic Acids Research<\/em>, September 2016. [link<\/a>]<\/li>\n
                    3. C.-A. Azencott. Network-guided biomarker discovery. In\u00a0Machine Learning for Health Informatics<\/em>, Lecture Notes in Computer Science 9605:319-336, 2016. [link<\/a>]<\/li>\n
                    4. Y. Jiao, A. Korba and E. Sibony. Controlling the distance to a Kemeny consensus without computing it.\u00a0Proceedings of the 33rd International Conference on Machine Learning<\/em>, pp. 2971-2980, New York, NY, USA, 2016. [link<\/a>]<\/li>\n
                    5. S. K. Sieberts, F. Zhu […] L. M. Mangravite.\u00a0Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.\u00a0Nature Communications<\/em>\u00a07:12460,\u00a02016. [link<\/a>]<\/li>\n
                    6. V. Machairas, T. Baldeweck, T. Walter, E. Decenciere.\u00a0New General Features Based on Superpixels for Image Segmentation Learning. Proceedings of the 12th IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro<\/em>. Prague, Czech Republic. April, 2016. [link<\/a>]<\/li>\n
                    7. M. Isokane, T. Walter, R. Mahen, B. Nijmeijer, J.-K. H\u00e9rich\u00e9, K. Miura, S. Maffini, M. P. Ivanov, T. S. Kitajima, J.-M. Peters and\u00a0J. Ellenberg. ARHGEF17 is an essential spindle assembly checkpoint factor that targets Mps1 to kinetochores.\u00a0Journal of Cell Biology<\/em>, 212(6): 647-659,\u00a02016. [link<\/a>]<\/li>\n
                    8. K. Vervier, P. Mah\u00e9, M. Tournoud, J.-B. Veyrieras and J.-P. Vert. Large-scale Machine Learning for Metagenomics Sequence Classification.\u00a0Bioinformatics<\/em>, 32(7):1023-1032, 2016.\u00a0[link]<\/a><\/li>\n
                    9. V. Bellon, V. Stoven and C.-A. Azencott. Multitask feature selection with task descriptors.\u00a0Pacific Symposium on Biocomputing<\/em>\u00a021:261-272,\u00a02016\u00a0[link]<\/a><\/li>\n<\/ol>\n

                      2015<\/h2>\n
                        \n
                      1. N. Servant, N. Varoquaux, B. R. Lajoie, E. Viara, C.-J. Chen, J.-P.Vert, E. Heard, J. Dekker and E. Barillot. HiC-Pro: An optimized and flexible pipeline for Hi-C data processing.\u00a0Genome Biology<\/em>, 16:259, 2015.\u00a0[link]<\/a><\/li>\n
                      2. M. Moarii, V. Boeva, J.-P. Vert and F. Reyal. Changes in correlation between promoter regulation and\u00a0gene expression\u00a0in cancer.\u00a0BMC Genomics<\/em>, 16:873, 2015.\u00a0[link]<\/a><\/li>\n
                      3. M. Moarii, F. Reyal and J.-P. Vert. Integrative DNA methylation and gene expression analysis to assess the universality of the CpG island methylator phenotype.\u00a0Human Genomics<\/em>, 9:26, 2015.\u00a0[link]<\/a><\/li>\n
                      4. N. Shervashidze and F. Bach. Learning the structure for structured sparsity.\u00a0IEEE Transactions on Signal Processing<\/em>, 63(18):4894-4902, 2015.\u00a0[link]<\/a><\/li>\n
                      5. S. Gribkova.\u00a0Vector quantization and clustering in the presence of censoring.\u00a0Journal of Multivariate Analysis<\/em>, 140:220-233, 2015. [link<\/a>]<\/li>\n
                      6. L. Guyon, C. Lajaunie, F. Fer, R. Bhajun, E. Sulpice, G. Pinna, A. Campalans, J. P. Radicella, P. Rouillier, M. Mary, S. Combe, P. Obeid, J.-P. Vert and X. Gidrol. Phi-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays.\u00a0Scientific Reports<\/em>, 5:14221, 2015.\u00a0[link]<\/a><\/li>\n
                      7. F. Eduati, L. M. Mangravite et al. Prediction of human population responses to toxic compounds by a collaborative competition.\u00a0Nature Biotechnology<\/em>, 33(9):933-940, 2015.\u00a0[link]<\/a><\/li>\n
                      8. E. Bernard, L. Jacob, J. Mairal, E. Viara and J.-P. Vert. A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples.\u00a0BMC Bioinformatics<\/em>, i16:262, 2015.\u00a0[link]<\/a><\/li>\n
                      9. Y. Jiao and J.-P. Vert. The Kendall and Mallows Kernels for Permutations.\u00a0Proceedings of the 32nd International Conference on Machine Learning<\/em>, JMLR: W&CP 37, 1935-1944, 2015.\u00a0[link]<\/a><\/li>\n
                      10. N. Varoquaux, I. Liachko, F. Ay, J. Burton, J. Shendure, M. Dunham, J.-P. Vert and W. S. Noble. Accurate identification of centromere locations in yeast genomes using Hi-C.\u00a0Nucleic Acids Research<\/em>, 43(11):5331-5339, 2015.\u00a0[link]<\/a><\/li>\n
                      11. D. G. Grimm, C.-A. Azencott, F. Aicheler, U. Gieraths, D. G. MacArhur, K. E. Samocha, D. N. Cooper, P. D. Stenson, M. J. Daly, J. W. Smoller, L. E. Duncan, K. M. Borgwardt. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity.\u00a0Human Mutation<\/em>, 36(5):513-523, 2015.\u00a0[link]<\/a><\/li>\n
                      12. V. Machairas, M. Faessel, D. Cardenas-Pena, T. Chabardes, T. Walter, E. Decenciere. Waterpixels. IEEE Transactions on Image Processing, 24(11): 3707-3716, Jul 2015. A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert and T. Walter. A generic methodological framework for studying single cell motility in high-throughput time-lapse data.\u00a0Bioinformatics<\/em>, 31(12):i320-i328, 2015.\u00a0[link]<\/a><\/li>\n
                      13. E. Scornet, G. Biau and J.-P. Vert. Consistency of random forests.\u00a0Annals of Statistics<\/em>, 43(4):1716-1741, 2015.\u00a0[link]<\/a><\/li>\n
                      14. V. Machairas, E. Decenciere and T. Walter. Spatial Repulsion Between Markers Improves Watershed Performance.\u00a0Mathematical Morphology and Its Applications to Signal and Image Processing<\/em>, Lecture Notes in Computer Science, 9082:194-202, 2015.\u00a0[link]<\/a><\/li>\n
                      15. A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert and T. Walter. Inferring an ontology of single cell motions from high-throughput microscopy data.\u00a0Proceedings of the 2015 IEEE International Symposium on Biomedical Imaging<\/em>, 160-163, 2015.\u00a0[link]<\/a><\/li>\n
                      16. F. Ay, T. H. Vu, M. J. Zeitz, N. Varoquaux, J. E. Carette, J.-P. Vert, A. R. Hoffman and W. S. Noble. Identifying multi-locus chromatin contacts in human cells using tethered multiple 3C.\u00a0BMC Genomics<\/em>, 16:121, 2015.\u00a0[link]<\/a><\/li>\n
                      17. M. Veta, P. J. van Diest, S. M. Willems, H. Wang, A. Madabhushi, A. Cruz-Roa, F. Gonzalez, A. B.L. Larsen, J. S. Vestergaard, A. B. Dahl, D. C. Cireşan, J. Schmidhuber, A. Giusti, L. M. Gambardella, F. Boray Tek, T. Walter, C.-W. Wang, S. Kondo, B. J. Matuszewski, F. Precioso, V. Snell, J. Kittler, T. E. de Campos, A. M. Khan, N. M. Rajpoot, E. Arkoumani, M. M. Lacle, M. A. Viergever, J. P.W. Pluim. Assessment of algorithms for mitosis detection in breast cancer histopathology images.\u00a0Medical Image Analysis<\/em>, 20(1):237-248, 2015.\u00a0[link]<\/a><\/li>\n
                      18. R. Bhajun, L. Guyon, A. Pitaval, E. Sulpice, S. Combe, P. Obeid, V. Haguet, I. Ghorbel, C. Lajaunie and X. Gidrol. A statistically inferred microRNA network identifies breast cancer target miR-940 as an actin cytoskeleton regulator.\u00a0Scientific Reports<\/em>, 5:8336, 2015.\u00a0[link]<\/a><\/li>\n
                      19. F. Ay, E. Bunnik, N. Varoquaux, J.-P. Vert, W. S. Noble and K. Le Roch. Multiple dimensions of epigenetic gene regulation in the malaria parasite Plasmodium falciparum.\u00a0BioEssays<\/em>, 37(2):182-194, 2015.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                        2014<\/h2>\n
                          \n
                        1. V Graml, X Studera, JLD Lawson, A Chessel, M Geymonat, M Bortfeld-Miller, T Walter, L Wagstaff, E Piddini and RE Carazo-Salas. A Genomic Multiprocess Survey of Machineries that Control and Link Cell Shape, Microtubule Organization, and Cell-Cycle Progression.\u00a0Developmental Cell<\/em>\u00a0, 31(2):227-239, 2014.\u00a0[link]<\/a><\/li>\n
                        2. V. Machairas, T. Walter, E. Decenciere. Waterpixels: Superpixels based on the watershed transformation. IEEE International Conference on Image Processing (ICIP) p. 4343 – 4347, November 2014. J Tegha-Dunghu, E Bausch, B Neumann, A Wuensche, T Walter, J Ellenberg, OJ Gruss. MAP1S controls microtubule stability throughout the cell cycle in human cells.\u00a0Journal of Cell Science<\/em>, 127:5007-5013, 2014.\u00a0[link]<\/a><\/li>\n
                        3. E. Richard, G. Obozinski and J.-P. Vert. Tight convex relaxations for sparse matrix factorization. In Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger (Eds.),\u00a0Advances in Neural Information Processing Systems 27<\/em>, 3284-3292, 2014.\u00a0[link]<\/a><\/li>\n
                        4. J.-K. H\u00e9rich\u00e9, J.G. Lees, I. Morilla, T. Walter, B. Petrova, M.J. Roberti, M.J. Hossain, P. Adler, J.M. Fern\u00e1ndez, M. Krallinger, C.H. Haering, J. Vilo, A. Valencia, J.A. Ranea, C. Orengo, J. Ellenberg. Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.\u00a0Molecular Biology of the Cell<\/em>, 25:2522\u20132536, Aug 2014.\u00a0[link]<\/a><\/li>\n
                        5. M. Moarii, A. Pinheiro, B. Sigal-Zafrani, A. Fourquet, M. Caly, N. Servant, V. Stoven J.-P. Vert and F. Reyal. Epigenomic alterations in breast carcinoma from primary tumor to locoregional recurrences.\u00a0PLoS ONE<\/em>, 9(8):e103986, 2014.\u00a0[link]<\/a><\/li>\n
                        6. K. Vervier, P. Mah\u00e9, A. d’Aspremont, J.-B. Veyrieras and J.-P. Vert. On learning matrices with orthogonal columns or disjoint supports. In T. Calder et al. (Eds),\u00a0ECML PKDD 2014<\/em>, Part III, LNCS 8726, 274-289, Springer-Verlag Berlin Heidelberg, 2014.\u00a0[link]<\/a><\/li>\n
                        7. J. C. Costello, L. M. Heiser, E. Georgii, M. G\u00f6nen, M. P Menden, N. J Wang, M. Bansal, M. Ammad-ud-din, P. Hintsanen, S. A Khan, J.-P. Mpindi, O. Kallioniemi, A. Honkela, T. Aittokallio, K. Wennerberg, NCI DREAM Community, J. J. Collins, D. Gallahan, D. Singer, J. Saez-Rodriguez, S. Kaski, J. W. Gray and G. Stolovitzky. A community effort to assess and improve drug sensitivity prediction algorithms.\u00a0Nature Biotechnology<\/em>, 32:1202-1212, 2014.\u00a0[link]<\/a><\/li>\n
                        8. E. Bernard, L. Jacob, J. Mairal and J.-P. Vert. Efficient RNA isoform identification and quantification from RNA-seq data with network flows.Bioinformatics<\/em>, 30(17):2447-2455, 2014.\u00a0[link]<\/a><\/li>\n
                        9. F. Ay, E. M. Bunnik, N. Varoquaux, S. M. Bol, J. Prudhomme, J.-P. Vert, W. S. Noble and K. G. Le Roch. Three-dimensional modeling of the P. falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression.\u00a0Genome Research<\/em>, 24:974-988, 2014.\u00a0[link]<\/a><\/li>\n
                        10. C. Tourette, F. Farina, R. P. Vazquez-Manrique, A.-M. Orfila, S. Hernandez, N. Offner, J. A. Parker, S. Menet, J. Kim, J. Lyu, S. H. Choi, K. Cormier, C. K. Edgerly, O. L. Bordiuk, K. Smith, A. Louise, M. Halford, S. Stacker, J.-P. Vert, R. J. Ferrante, W. Lu and C. Neri. Increase in the Wnt receptor Ryk promotes the early stage decline of mutant polyglutamine neurons by repressing FOXO protective activity.\u00a0PLoS Biology<\/em>, 12(6):e1001895, 2014.\u00a0[link]<\/a><\/li>\n
                        11. E. Pauwels, C. Lajaunie and J.-P. Vert. A Bayesian active learning strategy for sequential experimental design in systems biology.\u00a0BMC Systems Biology<\/em>, 8:102, 2014.\u00a0[link]<\/a><\/li>\n
                        12. N. Varoquaux, F. Ay, W. S. Noble and J.-P. Vert. A statistical approach for inferring the three-dimensional structure of the genome.\u00a0Bioinformatics<\/em>, 30(12):i26-i33, 2014.\u00a0[link]<\/a><\/li>\n
                        13. T. D. Hocking, V. Boeva, G. Rigaill, G. Schleiermacher, I. Janoueix-Lerosey, O. Delattre, W. Richer, F. Bourdeaut, M. Suguro, M. Seto, F. Bach and J.-P. Vert. SegAnnDB: interactive web-based genomic segmentation.\u00a0Bioinformatics<\/em>, 30(11):1539-1546, 2014.\u00a0[link]<\/a><\/li>\n
                        14. J.-L. Plouhinec, D. D. Roche, C. Pegoraro, A.-L. Figueiredo, F. Maczkowiak, L. J. Brunet, M. Cecile, J.-P. Vert, N. Pollet, R. M. Harland and A.-H. Monsoro-Burq. Pax3 and Zic1 trigger the early neural crest gene regulatory network by the direct activation of multiple key neural crest specifiers.\u00a0Developmental Biology<\/em>, 386(2):461-472, 2014.\u00a0[link]<\/a><\/li>\n
                        15. F. Mordelet and J.-P. Vert, \u00ab\u00a0A bagging SVM to learn from positive and unlabeled examples.\u00a0Pattern Recognition Letters<\/em>, 37:201-209, 2014.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                          2013<\/h2>\n
                            \n
                          1. Pau, G., Walter T., Neumann B., H\u00e9rich\u00e9 H.-K., Ellenberg J., Huber W. Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay\u00a0BMC Bioinformatics<\/em>, 14(1):308, Oct 2013.\u00a0[link]<\/a><\/li>\n
                          2. T. Traor\u00e9, A. Cavagnino, N. Saettel, F. Radvanyi, S. Piguel, I. Bernard-Pierrot, V. Stoven, M. Legraverend. New aminopyrimidine derivatives as inhibitors of the TAM family.\u00a0European Journal of Medicinal Chemistry<\/em>, 70:789-801, 2013\u00a0[link]<\/a><\/li>\n
                          3. A. Bouillon, D. Giganti, C. Benedet, O. Gorgette, S. P\u00eatres, E. Crublet, C. Girard-Blanc, B. Witkowski, D. M\u00e9nard, M. Nilges, O. Mercereau-Puijalon, V. Stoven, J.C. Barale. In silido screening on the three-dimensional model of the plasmodium vivax SUB1 protease leads to the validation of a novel anti-parasite compound.\u00a0Journal of Biological Chemistry<\/em>, 288: 18561-18573, 2013.\u00a0[link]<\/a><\/li>\n
                          4. Y. Zhao, T. Tamura, T. Akutsu and J-P. Vert. Flux balance impact degree: A new definition of impact degree to properly treat reversible reactions in metabolic networks.\u00a0Bioinformatics<\/em>, 29(17):2178-2185, 2013.\u00a0[link]<\/a><\/li>\n
                          5. T. D. Hocking, G. Schleiermacher, I. Janoueix-Lerosey, V. Boeva, J. Cappo, O. Delattre, F. Bach and J.-P. Vert. Learning smoothing models of copy number profiles using breakpoint annotations.\u00a0BMC Bioinformatics<\/em>, 14:164, 2013.\u00a0[link]<\/a><\/li>\n
                          6. E. Richard, F. Bach and J.-P. Vert. Intersecting singularities for multi-structured estimation. In S. Dasgupta and D. McAllester (Eds.),\u00a0Proceedings of the 30th International Conference on Machine Learning<\/em>, JMLR W&CP 28(3):1157-1165, 2013.\u00a0[link]<\/a><\/li>\n
                          7. G. Rigaill, T. D. Hocking, F. Bach and J.-P. Vert. Learning sparse penalties for change-point detection using max margin interval regression. In S. Dasgupta and D. McAllester (Eds.),\u00a0Proceedings of the 30th International Conference on Machine Learning<\/em>, JMLR W&CP 28(3):172-180, 2013.\u00a0[link]<\/a><\/li>\n
                          8. R.M Suarez, F. Chevot, A. Cavagnino, N. Saettel, F. Radvanyi, S. Piguel, I. Bernard-Pierrot, V. Stoven, M. Legraverend. Inhibitors of the TAM subfamily of tyrosine kinases: Synthesis and biological evaluation.\u00a0European Journal of Medicinal Chemistry<\/em>, 61:2-25, 2013.\u00a0[link]<\/a><\/li>\n
                          9. F. Mordelet and J.-P. Vert. Supervised inference of gene regulatory networks from positive and unlabeled examples. In H. Mamitsuka, C. DeLisi and M. Kanehisa (Eds),\u00a0Data Mining for Systems Biology, Methods in Molecular Biology 939<\/em>, Humana Press, p.47-58, 2013.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                            2012<\/h2>\n
                              \n
                            1. N. Servant,\u00a0M. A.\u00a0Bollet,\u00a0H. Halfwerk,\u00a0K. Bleakley,\u00a0B. Kreike,\u00a0L. Jacob,\u00a0D. Sie,\u00a0R. M.\u00a0Kerkhoven,\u00a0P. Hup\u00e9,\u00a0R. Hadhri,\u00a0A. Fourquet,\u00a0H. Bartelink,\u00a0E. Barillot,\u00a0B. Sigal-Zafrani\u00a0and\u00a0M. J.\u00a0van de Vijver.\u00a0Search for a Gene Expression Signature of Breast Cancer Local Recurrence in Young Women.\u00a0Clinical Cancer Research<\/em>, 18(6):1704-1715, 2012. [link<\/a>]<\/li>\n
                            2. A.-C. Haury, F. Mordelet, P. Vera-Licona and J.-P. Vert. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection.\u00a0BMC Sytems Biology<\/em>, 6:145, 2012.\u00a0[link]<\/a><\/li>\n
                            3. O. Filhol, D. Ciais, C. Lajaunie, P. Charbonnier, N. Foveau, J.-P. Vert and Y. Vandenbrouck. DSIR: Assessing the design of highly potent siRNA by testing a set of cancer-relevant target genes.\u00a0PLoS ONE<\/em>, 7(10):e48057\u00a0[link]<\/a><\/li>\n
                            4. . Mall, M., Walter, T., Gorj\u00e1n\u00e1cz, M., Davidson, I.F., Ly-Hartig, N., Ellenberg, J., & Mattaj, I.W. Mitotic lamin disassembly is triggered by lipidmediated signaling.\u00a0Journal of Cell Biology<\/em>, 198(6):981-990, 2012.\u00a0[ link]<\/a><\/li>\n
                            5. S. Mizutani, E. Pauwels, V. Stoven, S. Goto, and Y. Yamanishi. Relating drug-protein interaction network with drug side-effects.\u00a0Bioinformatics<\/em>, 28(18):i522-i528, 2012.\u00a0[link]<\/a><\/li>\n
                            6. Y. Tabei, E. Pauwels, V. Stoven, K. Takemoto, and Y. Yamanishi. Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers.\u00a0Bioinformatics<\/em>, 28(18):i487-i494, 2012.\u00a0[link]<\/a><\/li>\n
                            7. E. Pauwels, D. Surdez, G. Stoll, A. Lescure, E. Del Nery, O. Delattre, V. Stoven. A Probabilistic Model for Cell Population Phenotyping Using HCS Data.\u00a0PLoS ONE<\/em>, 7(8):e42715, 2012.\u00a0[link]<\/a><\/li>\n
                            8. D. Marbach, J.C. Costello, R. Kuffner, N. Vega, R.J. Prill, D.M. Camacho, K.R. Allison, the DREAM5 Consortium, M. Kellis, J.J. Collins, G. Stolovitzky. Wisdom of crowds for robust gene network inference.\u00a0Nature Methods<\/em>, 9:796-804, 2012.\u00a0[link]<\/a><\/li>\n
                            9. C. Houdayer, V. Caux-Moncoutier, S. Krieger, M. Barrois, F. Bonnet, V. Bourdon, M. Bronner, M. Buisson, F. Coulet, P. Gaildrat, C. Lefol, M. L\u00e9one, S. Mazoyer, D. Muller, A. Remenieras, F. R\u00e9villion, E. Rouleau, J. Sokolowska, J.-P. Vert, R. Lidereau, F. Soubrier, H. Sobol, N. Sevenet, B. Bressac de Paillerets, A. Hardouin, M. Tosi, O.M. Sinilnikova and D. Stoppa-Lyonnet. Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico\/in vitro studies on BRCA1 and BRCA2 variants.\u00a0Human Mutation<\/em>, 33(8):1228-1238, 2012.\u00a0[link]<\/a><\/li>\n
                            10. F. Lejeune, L. Mesrob, F. Parmentier, C. Bicep, R. Vazquez, A. Parker, J.-P. Vert, C. Tourette and C. Neri. Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons.\u00a0BMC Genomics<\/em>, 13:91, 2012.\u00a0[link]<\/a><\/li>\n
                            11. V. Boeva, T. Popova, K. Bleakley, P. Chiche, J. Cappo, G. Schleiermacher, I. Janoueix-Lerosey, O. Delattre and E. Barillot. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data.\u00a0Bioinformatics<\/em>, 28(3):423-425, 2010.\u00a0[link]<\/a><\/li>\n
                            12. K. Takemoto, T. Tamura, Y. Cong, W.-K. Ching, J.-P. Vert and T. Akutsu. Analysis of the impact degree distribution in metabolic networks using branching process approximation.\u00a0Physica A<\/em>, 391(1-2):379-387, 2012.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                              2011<\/h2>\n
                                \n
                              1. A.-C. Haury, P. Gestraud and J.-P. Vert, \u00ab\u00a0The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures\u00a0\u00bb,\u00a0PLoS ONE<\/em>, 6(12):e28210, 2011.\u00a0[link]<\/a><\/li>\n
                              2. F. Mordelet and J.-P. Vert, \u00ab\u00a0ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 12:389, 2011.\u00a0[link]<\/a><\/li>\n
                              3. T. Matsui, M. Goto, J.-P. Vert and Y. Uchiyama, \u00ab\u00a0Gradient-based musical feature extraction based on scale-invariant feature transform\u00a0\u00bb, in\u00a0Proceedings of the 19th European Signal Processing Conference (EUSIPCO 2011)<\/em>, p.724-728, 2011.\u00a0[pdf]<\/a><\/li>\n
                              4. T. Hocking, A. Joulin, F. Bach and J.-P. Vert, \u00ab\u00a0Clusterpath: an algorithm for clustering using convex fusion penalties\u00a0\u00bb, in L. Getoor and T. Scheffer (Eds.),\u00a0Proceedings of the 28th International Conference on Machine Learning (ICML-11)<\/em>, p.745-752, ACM, New-York, NY, USA, 2011.\u00a0[pdf]<\/a><\/li>\n
                              5. E. Pauwels, V. Stoven, and Y. Yamanishi, \u00ab\u00a0Predicting drug side-effect profiles: a chemical fragment-based approach\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 12:169, 2011.\u00a0[link]<\/a><\/li>\n
                              6. Y. Yamanishi, E. Pauwels, H. Saigo, and V. Stoven, \u00ab\u00a0Extracting sets of chemical substructures and protein domains governing drug-target interactions\u00a0\u00bb,\u00a0Journal of Chemical Information and Modeling<\/em>, 51 (5), pp 1183\u20131194, 2011.\u00a0[link]<\/a><\/li>\n
                              7. V. Boeva, A. Zinovyev, K. Bleakley, J.-P. Vert, I. Janoueix-Lerosey, O. Delattre and E. Barillot, \u00ab\u00a0Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization\u00a0\u00bb,\u00a0Bioinformatics<\/em>\u00a027(2):268-269, 2011.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                                2010<\/h2>\n
                                  \n
                                1. K. Bleakley and J.-P. Vert, \u00ab\u00a0Fast detection of multiple change-points shared by many signals using group LARS\u00a0\u00bb, in J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R.S. Zemel and A. Culotta (Eds),\u00a0Advances in Neural Information Processing Systems 23 (NIPS)<\/em>, p.2343-2351, 2010.\u00a0[pdf]<\/a><\/li>\n
                                2. M. Zaslavskiy, F. Bach and J.-P. Vert, \u00ab\u00a0Many-to-many graph matching: a continuous relaxation approach\u00a0\u00bb, in J. Balcazar, F. Bonchi and A. Gionis (Eds.),\u00a0Machine Learning and Knowledge Discovery in Databases (Proceedings of ECML\/PKDD 2010)<\/em>, LNCS 6323, p.515-530, Springer, 2010.\u00a0[link]<\/a><\/li>\n
                                3. T. Tamura, Y. Yamanishi, M. Tanabe, S. Goto, M. Kanehisa, K. Horimoto, and T. Akutsu, \u00ab\u00a0Integer programming-based method for completing signaling pathways and its application to analysis of colorectal cancer\u00a0\u00bb,\u00a0Genome Informatics (Proceedings of IBSB2010)<\/em>, Vol.24, pp.193-203, 2010.\u00a0 H. Lodhi and Y. Yamanishi (Eds.),\u00a0Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques<\/em>, IGI Global, 2010\u00a0[link]<\/a><\/li>\n
                                4. Y. Yamanishi and H. Kashima, \u00ab\u00a0Prediction of compound-protein interactions with machine learning methods\u00a0\u00bb, in H. Lodhi and Y. Yamanishi (Eds.),\u00a0Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques<\/em>, p.304-317, IGI Global, 2010.<\/li>\n
                                5. J.-P. Vert, \u00ab\u00a03D ligand-based virtual screening with support vector machines\u00a0\u00bb, in H. Lodhi and Y. Yamanishi (Eds.),\u00a0Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques<\/em>, p.35-45, IGI Global, 2010.<\/li>\n
                                6. K. Schauer, T. Duong, K. Bleakley, S. Bardin, M. Bornens and B. Goud, \u00ab\u00a0Construction of probabilistic density maps for micropatterned cells: a novel method to study the global cellular architecture of endomembranes\u00a0\u00bb,\u00a0Nature Methods<\/em>, 7, p. 560\u2013566, 2010.<\/li>\n
                                7. H. Kashima, S. Oyama, Y. Yamanishi and K. Tsuda, \u00ab\u00a0Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel\u00a0\u00bb,\u00a0IEICE Trans Inf Syst<\/em>, E93D(10):2672-2679, 2012.<\/li>\n
                                8. M. Hue and J.-P. Vert, \u00ab\u00a0On learning with kernels for unordered pairs\u00a0\u00bb, in J. Furnkranz and T. Joachims (Eds.),\u00a0Proceedings of the 27th International Conference on Machine Learning (ICML)<\/em>, p.463-470, 2010.\u00a0[pdf]<\/a><\/li>\n
                                9. Y. Yamanishi, M. Kotera, M. Kanehisa, and S. Goto, \u00ab\u00a0Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework\u00a0\u00bb,\u00a0Bioinformatics (Proceedings of ISMB2010)<\/em>, 26: i246-i254, 2010.\u00a0[link]<\/a><\/li>\n
                                10. G. Biau, K. Bleakley, L. Gy\u00f6rfi and G. Ottucs\u00e1k, \u00ab\u00a0Nonparametric sequential prediction of time series\u00a0\u00bb,\u00a0Journal of Nonparametric Statistics<\/em>, 22(3), p. 297-317, 2010.<\/li>\n
                                11. M. Hue, M. Riffle, J.-P. Vert and W.S. Noble,\u00a0\u00bbLarge-scale prediction of protein-protein interactions from structures\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 11:144, 2010.<\/li>\n
                                12. B. Hoffmann, M. Zaslavskiy, J.-P. Vert and V. Stoven, \u00ab\u00a0A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 11:99, 2010. Y. Yamanishi, \u00ab\u00a0Supervised inference of metabolic networks from the integration of genomic data and chemical information\u00a0\u00bb, in H. Lodhi and S. Muggleton (Eds.),\u00a0Elements of Computational Systems Biology<\/em>, Wiley, p.189-212, 2010.<\/li>\n
                                13. J.-P. Vert, \u00ab\u00a0Reconstruction of biological networks by supervised machine learning approaches\u00a0\u00bb, in H. Lodhi and S. Muggleton (Eds.),\u00a0Elements of Computational Systems Biology<\/em>, Wiley, p.165-188, 2010.<\/li>\n<\/ol>\n

                                  2009<\/h2>\n
                                    \n
                                  1. M. Cuturi, J.-P. Vert and A. d’Aspremont, \u00ab\u00a0White Functionals for Anomaly Detection in Dynamical Systems\u00a0\u00bb, in Y. Bengio, D. Schuurmans, J. Lafferty, C.K.I. Williams and A. Culotta (Eds),\u00a0Advances in Neural Information Processing Systems 22 (NIPS)<\/em>, p.432-440, 2009.\u00a0[pdf]<\/a><\/li>\n
                                  2. F. Austerlitz, O. David, B. Schaeffer, K. Bleakley, M. Olteanu, R. Leblois, M. Veuille and C. Laredo, \u00ab\u00a0DNA barcode analysis: a comparison of phylogenetic and statistical classification methods\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 10(Suppl 14):S10, 2009.\u00a0[link]<\/a><\/li>\n
                                  3. H. Kashima, Y. Yamanishi, T. Kato, M. Sugiyama, and K. Tsuda, \u00ab\u00a0Simultaneous Inference of Biological Networks of Multiple Species from Genome-wide Data and Evolutionary Information: A Semi-supervised Approach\u00a0\u00bb,\u00a0Bioinformatics<\/em>, Vol.25, p.2962-2968, 2009.\u00a0[link]<\/a><\/li>\n
                                  4. Duclert-Savatier N., Poggi L., Lopes P., Chevalier N., Nilges M., Delarue M. and Stoven V. \u00ab\u00a0Insights into the enzymatic mechanism of 6-phosphogluconolactonase from Trypanosoma brucei using structural data and molecular dynamics simulation\u00a0\u00bb.\u00a0Journal of Molecular Biology<\/em>, 388(5):1009-21, 2009.<\/li>\n
                                  5. M. Zaslavskiy, M. Dymetman and N. Cancedda \u00ab\u00a0Phrase-Based Statistical Machine Translation as a Traveling Salesman Problem\u00a0\u00bb,\u00a0Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2009)<\/em>, p.333-341, 2009.<\/li>\n
                                  6. M. Zaslavskiy, F. Bach and J.-P. Vert, \u00ab\u00a0A path following algorithm for the graph matching problem\u00a0\u00bb,\u00a0IEEE Transactions on Pattern Analysis and Machine Intelligence<\/em>, 31(12):2227-2242, 2009.\u00a0[link]<\/a><\/li>\n
                                  7. J.-P. Vert, T. Matsui, S. Satoh and Y. Uchiyama, \u00ab\u00a0High-level feature extraction using SVM with walk-based graph kernel\u00a0\u00bb,\u00a0Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)<\/em>, p. 1121-1124, 2009.<\/li>\n
                                  8. L. Jacob, G. Obozinski, J.-P. Vert, \u00ab\u00a0Group Lasso with Overlaps and Graph Lasso\u00a0\u00bb, in L. Bottou and M. Littman, (Ed.),\u00a0Proceedings of the 26th International Conference on Machine Learning<\/em>, 433-440, 2009.\u00a0[link]<\/a><\/li>\n
                                  9. P. Mah\u00e9 and J.-P. Vert, \u00ab\u00a0Virtual screening with support vector machines and structure kernels\u00a0\u00bb,\u00a0Combinatorial Chemistry & High Throughput Screening<\/em>, 12(4):409-423, 2009.\u00a0[link]<\/a><\/li>\n
                                  10. K. Bleakley and Y. Yamanishi, \u00ab\u00a0Supervised prediction of drug-target interactions using bipartite local models\u00a0\u00bb,\u00a0Bioinformatics<\/em>, Vol.25, pp.2397-2403, 2009.\u00a0[link]<\/a><\/li>\n
                                  11. M. Zaslavskiy, F. Bach and J.-P. Vert, \u00ab\u00a0Global alignment of protein-protein interaction networks by graph matching methods\u00a0\u00bb,\u00a0Bioinformatics (Proceedings of ISMB\/ECCB2009)<\/em>, Vol.25, pp.i259-1267, 2009.\u00a0[link]<\/a><\/li>\n
                                  12. Y. Yamanishi, M. Hattori, M. Kotera, S. Goto, and M. Kanehisa, \u00ab\u00a0E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs\u00a0\u00bb,\u00a0Bioinformatics (Proceedings of ISMB\/ECCB2009)<\/em>, Vol.25, pp.i179-i186, 2009.\u00a0[link]<\/a><\/li>\n
                                  13. H. Kashima, S. Oyama, Y. Yamanishi, and K. Tsuda, \u00ab\u00a0On Pairwise Kernels: An Efficient Alternative and Generalization Analysis\u00a0\u00bb,\u00a0Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)<\/em>, Vol.5476, pp.1030-1037, 2009.\u00a0[link]<\/a><\/li>\n
                                  14. H. Kashima, T. Kato, Y. Yamanishi, M. Sugiyama, and K. Tsuda, \u00ab\u00a0Link Propagation: A Fast-semi Supervised Learning Algorithm for Link Prediction\u00a0\u00bb,\u00a0Proceedings of the 9th SIAM Conference on Data Mining (SDM)<\/em>, pp.1099-1110, 2009.\u00a0[link]<\/a><\/li>\n
                                  15. Y. Yamanishi, \u00ab\u00a0Supervised Bipartite Graph Inference\u00a0\u00bb,\u00a0Advances in Neural Information Processing Systems 21<\/em>, D. Koller, D. Schuurmans, Y. Bengio and L. Bottou (Eds.), pp.1841-1848, MIT Press, Cambridge, MA, 2009.\u00a0[link]<\/a><\/li>\n
                                  16. L. Jacob, F. Bach and J.-P. Vert, \u00ab\u00a0Clustered multitask learning: a convex formulation\u00a0\u00bb,\u00a0Advances in Neural Information Processing Systems 21<\/em>, D. Koller, D. Schuurmans, Y. Bengio and L. Bottou (Eds.), pp.745-752, MIT Press, Cambridge, MA, 2009.\u00a0[pdf]<\/a><\/li>\n
                                  17. J. Abernethy, F. Bach, T. Evgeniou and J.-P. Vert, \u00ab\u00a0A new approach to collaborative filtering: operator estimation with spectral regularization\u00a0\u00bb,\u00a0Journal of Machine Learning Research<\/em>, 10:803-826, 2009.\u00a0[link]<\/a><\/li>\n
                                  18. P. Mah\u00e9 and J.-P. Vert, \u00ab\u00a0Graph kernels based on tree patterns for molecules\u00a0\u00bb,\u00a0Machine Learning<\/em>, 75(1):3-35, 2009.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                                    2008<\/h2>\n
                                      \n
                                    1. K. Bleakley, M.-P. Lefranc and G. Biau, \u00ab\u00a0Recovering probabilities for nucleotide trimming processes for T cell receptor TRA and TRG V-J junctions analysed with IMGT tools\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 9:408, 2008.\u00a0[link]<\/a><\/li>\n
                                    2. T.E. Malliavin, H. Munier-Lehman, V. Stoven. \u00ab\u00a0Virtual screening of the guanylate Monophosphate Kinase (GMPK) family: investigating the rules of ligand specificity.\u00a0\u00bb\u00a0Letters in Drug Design & Discovery<\/em>, 5(5):319-326.\u00a0[link]<\/a><\/li>\n
                                    3. L. Jacob and J.-P. Vert, \u00ab\u00a0Protein-ligand interaction prediction: an improved chemogenomics approach\u00a0\u00bb,\u00a0Bioinformatics<\/em>, 24(19):2149-2156, 2008.\u00a0[link]<\/a><\/li>\n
                                    4. L. Jacob, B. Hoffmann, V. Stoven and J.-P. Vert, \u00ab\u00a0Virtual screening of GPCRs: an in silico chemogenomics approach\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 9:363, 2008.\u00a0[link]<\/a><\/li>\n
                                    5. J.-P. Vert and L. Jacob, \u00ab\u00a0Machine learning for in silico virtual screening and chemical genomics: new strategies\u00a0\u00bb,\u00a0Combinatorial Chemistry & High Throughput Screening<\/em>, 11(8):677-685, 2008.\u00a0[link]<\/a><\/li>\n
                                    6. F. Mordelet and J.-P. Vert, \u00ab\u00a0SIRENE: Supervised Inference of REgulatory NEtworks\u00a0\u00bb,\u00a0Bioinformatics<\/em>, 24(16):i76-i82, 2008.\u00a0[link]<\/a><\/li>\n
                                    7. F. Rapaport, E. Barillot and J.-P. Vert, \u00ab\u00a0Classification of arrayCGH data using a fused SVM\u00a0\u00bb,\u00a0Bioinformatics<\/em>, 24(13):i365-i382, 2008.\u00a0[link]<\/a><\/li>\n
                                    8. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda and M. Kanehisa, \u00ab\u00a0Prediction of drug-target interaction networks from the integration of chemical and genomic spaces\u00a0\u00bb,\u00a0Bioinformatics<\/em>, 24(13):i232-240, 2008.\u00a0[link]<\/a><\/li>\n
                                    9. M. Zaslavskiy, F. Bach and J.-P. Vert, \u00ab\u00a0A path following algorithm for graph matching\u00a0\u00bb, in A. Elmoataz, O. Lezoray, F. Nouboud and D. Mammass (Eds.),\u00a0Proceedings of the 3rd International Conference on Image and Signal Processing (ICISP 2008)<\/em>, LNCS 5099:329-337, 2008.\u00a0[link]<\/a><\/li>\n
                                    10. L. Jacob and J.-P. Vert, \u00ab\u00a0Efficient peptide-MHC-I binding prediction for alleles with few known binders\u00a0\u00bb,\u00a0Bioinformatics<\/em>, 24(3):358-366, 2008\u00a0[link]<\/a><\/li>\n
                                    11. [server]<\/a><\/li>\n
                                    12. J. Abernethy, T. Evgeniou, O. Toubia and J.-P. Vert, \u00ab\u00a0Eliciting consumer preferences using robust adaptive choice questionnaires\u00a0\u00bb,\u00a0IEEE Transactions on Knowledge and Data Engineering<\/em>, 20(2):145-155, 2008.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                                      2007<\/h2>\n
                                        \n
                                      1. J.-P. Vert, J. Qiu and W. S. Noble, \u00ab\u00a0A new pairwise kernel for biological network inference with support vector machines\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 8(Suppl 10):S8, 2007.\u00a0[link]<\/a><\/li>\n
                                      2. K. Bleakley, G. Biau and J.-P. Vert, \u00ab\u00a0Supervised network inference with local models\u00a0\u00bb,\u00a0Bioinformatics<\/em>, 23(13):i57-i65, 2007.\u00a0[link]<\/a><\/li>\n
                                      3. M. Cuturi, J.-P. Vert, O. Birkenes and T. Matsui, \u00ab\u00a0A kernel for time series based on global alignments\u00a0\u00bb,\u00a0Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)<\/em>\u00a0, 2:.II-413-II-416, 2007.\u00a0[link]<\/a><\/li>\n
                                      4. Y. Yamanishi, F. Bach and J.-P. Vert, \u00ab\u00a0Glycan classification with tree kernels\u00a0\u00bb,\u00a0Bioinformatics<\/em>, 23(10):1211-1216, 2007\u00a0[link]<\/a><\/li>\n
                                      5. J. Qiu, M. Hue, A. Ben-Hur, J.-P. Vert and W. S. Noble, \u00ab\u00a0A structural alignment kernel for protein structures\u00a0\u00bb, Bioinformatics<\/em>\u00a023(9):1090-1098, 2007[link]<\/a><\/li>\n
                                      6. Y. Yamanishi and J.-P. Vert, \u00ab\u00a0Kernel matrix regression\u00a0\u00bb, Proceedings of the 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007)<\/em>, 2007.<\/li>\n
                                      7. J.-P. Vert, \u00ab\u00a0Kernel methods in genomics and computational biology\u00a0\u00bb, in Camps-Valls, G., Rojo-Alvarez, J.-L. and Martinez-Ramon, M. (Eds.),\u00a0Kernel Methods in Bioengineering, Signal and Image Processing<\/em>, p.42-63, Idea Group, 2007.<\/li>\n
                                      8. F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot and J.-P. Vert, ‘\u00a0\u00bbClassification of microarray data using gene networks\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 8:35, 2007.\u00a0[link]<\/a><\/li>\n
                                      9. M. Delarue, N. Duclert-Savatier, E. Miclet, A. Haouz, D. Giganti, J. Ouazzani, P. Lopez, M. Nilges and V. Stoven, \u00ab\u00a0Three dimensional structure and implications for the catalytic mechanism of 6-phosphogluconolactonase from\u00a0Trypanosoma brucei<\/em>\u00ab\u00a0,\u00a0Journal of Molecular Biology<\/em>, 306(3):868-881, 2007.\u00a0[link]<\/a><\/li>\n
                                      10. F. Lemaire, C. A. Mandon, J. Reboud, A. Papine, J. Angulo, H. Pointu, C. Diaz-Latoud, C. Lajaunie, F. Chatelain, A.-P. Arrigo and B. Schaack, \u00ab\u00a0Toxicity assays in nanodrops combining bioassay and morphometric endpoints\u00a0\u00bb,\u00a0PLoS ONE<\/em>, 2(1):e163, 2007\u00a0[link]<\/a><\/li>\n<\/ol>\n

                                        2006<\/h2>\n
                                          \n
                                        1. Y. Yamanishi and J.P. Vert, \u00ab\u00a0Estimating Protein Network from Multiple Genomic Data by Kernel Methods\u00a0\u00bb,\u00a0Proceedings of the Institute of Statistical Mathematics<\/em>, 54(2):357:373, 2006\u00a0[pdf]<\/a><\/li>\n
                                        2. J.-P. Vert, N. Foveau, C. Lajaunie and Y. Vandenbrouck, \u00ab\u00a0An accurate and interpretable model for siRNA efficacy prediction\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>, 7:520, 2006.\u00a0[link]<\/a><\/li>\n
                                        3. F. Carrat , J. Luong , H. Lao , A.-V. Salle , C. Lajaunie and H. Wackernage, \u00ab\u00a0A \u00ab\u00a0small-world-like\u00a0\u00bb model for comparing interventions aimed at preventing and controlling influenza pandemics\u00a0\u00bb,\u00a0BMC Medicine<\/em>, 4:26, 2006\u00a0[link]<\/a><\/li>\n
                                        4. Y. Yamanishi and Y. Tanaka, \u00ab\u00a0Sensitivity Analysis in Kernel Principal Component Analysis\u00a0\u00bb, in A. Rizzi and M. Vichi (Eds.),\u00a0COMPSTAT 2006 – Proceedings in Computational Statistics<\/em>, p. 787-794, Physica-Verlag\/Springer, 2006.<\/li>\n
                                        5. P. Mah\u00e9, L. Ralaivola, V. Stoven and J.-P. Vert, \u00ab\u00a0The pharmacophore kernel for virtual screening with support vector machines\u00a0\u00bb,\u00a0Journal of Chemical Information and Modeling<\/em>, 46(5):2003-2014, 2006.\u00a0\u00a0[link]<\/a><\/li>\n
                                        6. J.-P. Vert, \u00ab\u00a0Classification of biological sequences with kernel methods\u00a0\u00bb, in Sakakibara et al. (Eds.),\u00a0Proceedings of ICGI 2006<\/em>, LNAI 4201, p.7-18, Springer Verlag, 2006.<\/li>\n
                                        7. K. Loth, D. Abergel, P. Pelupessy, M. Delarue, P. Lopes, J. Quazzani, N. Duclert-Savatier, M. Nilges, G. Bodenhausen and V. Stoven, \u00ab\u00a0Determination of hihedral Y angles in large proteins by combining NH\/CaHa dipole\/dipole cross correlation and chemical shifts\u00a0\u00bb,\u00a0Proteins: Structure, Function, and Bioinformatics<\/em>, 64(4):931-939, 2006.\u00a0[link]<\/a><\/li>\n
                                        8. R. Vert and J.-P. Vert, \u00ab\u00a0Consistency and convergence rates of one-class SVM and related algorithms\u00a0\u00bb,\u00a0Journal of Machine Learning Research<\/em>, 7:817-854, 2006.\u00a0[link]<\/a><\/li>\n
                                        9. H. Saigo, J.-P. Vert and T. Akutsu, \u00ab\u00a0Optimizing amino acid substitution matrices with a local alignment kernel\u00a0\u00bb,\u00a0BMC Bioinformatics<\/em>\u00a07:246, 2006.\u00a0[link]<\/a><\/li>\n
                                        10. J.-P. Vert, R. Thurman and W. S. Noble, \u00ab\u00a0Kernels for gene regulatory regions\u00a0\u00bb,\u00a0Advances in Neural Information Processing Systems 18<\/em>, Y. Weiss, B. Sch\u00f6lkopf and J. Platt (Eds.), p.1401-1408, MIT Press, Cambridge, MA, 2006.\u00a0[pdf]<\/a><\/li>\n
                                        11. R. Vert and J.-P. Vert, \u00ab\u00a0Consistency and convergence rates of one-class SVM and related algorithms\u00a0\u00bb,\u00a0Advances in Neural Information Processing Systems 18<\/em>, Y. Weiss, B. Sch\u00f6lkopf and J. Platt (Eds.), p.1409-1416, MIT Press, Cambridge, MA, 2006.\u00a0[pdf]<\/a><\/li>\n<\/ol>\n

                                          2005<\/h2>\n
                                            \n
                                          1. J. Vermorel and M. Mohry, \u00ab\u00a0Multi-armed Bandit Algorithm and Empirical Evaluation\u00a0\u00bb, in\u00a0Proceedings of the 16th European Conference of Machine Learning (ECML’05)<\/em>, vol. 3720 of\u00a0Lecture Notes in Computer Science<\/em>, p. 437-448, Springer, Heidelberg, Germany, 2005.\u00a0[link]<\/a><\/li>\n
                                          2. . S. Matsuda, J.-P. Vert, H. Saigo, N. Ueda, H. Toh, and T. Akutsu, \u00ab\u00a0A novel representation of protein sequences for prediction of subcellular location using support vector machines\u00a0\u00bb,\u00a0Protein Science<\/em>, vol. 14, p. 2804-2813, 2005.\u00a0[link]<\/a><\/li>\n
                                          3. [server]<\/a><\/li>\n
                                          4. M. Cuturi and J.-P. Vert, \u00ab\u00a0The context-tree kernel for strings\u00a0\u00bb,\u00a0Neural networks<\/em>, vol. 18, n. 4, p. 1111-1123, 2005.\u00a0[link]<\/a><\/li>\n
                                          5. T. Sato, Y. Yamanishi, M. Kanehisa and H. Toh, \u00ab\u00a0The inference of protein\u2013protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships\u00a0\u00bb,\u00a0Bioinformatics<\/em>, vol. 21(17), p.3482-3489, 2005.\u00a0[link]<\/a><\/li>\n
                                          6. M. Cuturi, K. Fukumizu and J.-P. Vert, \u00ab\u00a0Semigroup kernels on measures\u00a0\u00bb,\u00a0Journal of Machine Learning Research<\/em>, vol. 6, p. 1169-1198, 2005.\u00a0[link]<\/a><\/li>\n
                                          7. Y. Yamanishi, J.-P. Vert and M. Kanehisa, \u00ab\u00a0Supervised enzyme network inference from the integration of genomic data and chemical information\u00a0\u00bb,\u00a0Bioinformatics<\/em>, vol. 21, p. i468-i477, 2005.\u00a0[link]<\/a><\/li>\n
                                          8. P. Mah\u00e9, N. Ueda, T. Akutsu, J.-L. Perret and J.-P. Vert, \u00ab\u00a0Graph kernels for molecular structure-activity relationship analysis with support vector machines\u00a0\u00bb,\u00a0J. Chem. Inf. Model.<\/em>, vol. 45, n. 4, 939 -951, 2005.\u00a0[link]<\/a><\/li>\n
                                          9. M. Cuturi and J.-P. Vert, \u00ab\u00a0Semigroup kernels on finite sets\u00a0\u00bb,\u00a0Advances in Neural Information Processing Systems 17<\/em>, Lawrence K. Saul and Yair Weiss and L\u00e9on Bottou (Eds.), p.329-336, MIT Press, Cambridge, MA, 2005.\u00a0[pdf]<\/a><\/li>\n
                                          10. J.-P. Vert and Y. Yamanishi, \u00ab\u00a0Supervised graph inference\u00a0\u00bb,\u00a0Advances in Neural Information Processing Systems 17<\/em>, Lawrence K. Saul, Yair Weiss and L\u00e9on Bottou (Eds.), p.1433-1440, MIT Press, Cambridge, MA, 2005.\u00a0[pdf]<\/a><\/li>\n<\/ol>\n

                                            2004<\/h2>\n
                                              \n
                                            1. P. Mah\u00e9, N. Ueda, T. Akutsu, J.-L. Perret and J.-P. Vert, \u00ab\u00a0Extensions of marginalized graph kernels\u00a0\u00bb,\u00a0Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004)<\/em>, R. Greiner and D. Schuurmans (Eds.), p.552-559, ACM Press, 2004.\u00a0[pdf]<\/a><\/li>\n
                                            2. M. Cuturi, J.-P. Vert, \u00ab\u00a0A mutual information kernel for strings\u00a0\u00bb;,\u00a0Proceedings of the 2004 IEEE International Joint Conference on Neural Networks<\/em>, p. 1905-1910, 2004.\u00a0[pdf]<\/a><\/li>\n
                                            3. Y. Yamanishi, J.-P. Vert and M. Kanehisa, \u00ab\u00a0Protein network inference from multiple genomic data: a supervised approach\u00a0\u00bb,\u00a0Bioinformatics<\/em>, vol.20, p.i363-i370, 2004.\u00a0[link]<\/a><\/li>\n
                                            4. B. Sch\u00f6lkopf, K. Tsuda and J.-P. Vert (Eds.), \u00ab\u00a0Kernel Methods in Computational Biology\u00a0\u00bb,\u00a0MIT Press<\/em>, 2004.\u00a0[link]<\/a><\/li>\n
                                            5. J.-P. Vert, K. Tsuda and B. Sch\u00f6lkopf, \u00ab\u00a0A primer on kernel methods\u00a0\u00bb, in\u00a0Kernel Methods in Computational Biology, B. Sch\u00f6lkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press<\/em>, p.35-70, 2004.<\/li>\n
                                            6. J.-P. Vert, H. Saigo, T. Akutsu, \u00ab\u00a0Local alignment kernels for biological sequences\u00a0\u00bb, in\u00a0Kernel Methods in Computational Biology, B. Sch\u00f6lkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press<\/em>, p.131-154, 2004.<\/li>\n
                                            7. R. Kondor and J.-P. Vert, \u00ab\u00a0Diffusion kernels\u00a0\u00bb, in\u00a0Kernel Methods in Computational Biology, B. Sch\u00f6lkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press<\/em>, p.171-192, 2004.<\/li>\n
                                            8. Y. Yamanishi, J.-P. Vert and M. Kanehisa, \u00ab\u00a0Heterogeneous data comparison and gene selection with kernel canonical correlation analysis\u00a0\u00bb, in\u00a0Kernel Methods in Computational Biology, B. Sch\u00f6lkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press<\/em>, p.209-230, 2004.<\/li>\n
                                            9. H. Saigo, J.-P. Vert, T. Akutsu and N. Ueda, \u00ab\u00a0Protein homology detection using string alignment kernels\u00a0\u00bb,\u00a0Bioinformatics<\/em>, vol.20, p.1682-1689, 2004.\u00a0[link]<\/a><\/li>\n<\/ol>\n

                                              2003<\/h2>\n
                                                \n
                                              1. J.-P. Vert and M. Kanehisa, \u00ab\u00a0Extracting active pathways from gene expression data\u00a0\u00bb,\u00a0Bioinformatics<\/em>, vol. 19, p. 238ii-244ii, 2003.\u00a0[link]<\/a><\/li>\n
                                              2. Y. Yamanishi, J.-P. Vert, A. Nakaya and M. Kanehisa, \u00ab\u00a0Extraction of Correlated Gene Clusters from Multiple Genomic Data by Generalized Kernel Canonical Correlation Analysis\u00a0\u00bb,\u00a0Bioinformatics<\/em>, vol. 19, p. 323i-330i, 2003.\u00a0[link]<\/a><\/li>\n
                                              3. J.-P. Vert and M. Kanehisa, \u00ab\u00a0Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA\u00a0\u00bb,\u00a0Advances in Neural Information Processing Systems 15<\/em>, Suzanna Becker, Sebastian Thrun and Klaus Obermayer (Eds), p. 1425-1432, MIT Press, Cambridge, MA, 2003.\u00a0[pdf]<\/a><\/li>\n<\/ol>\n

                                                 <\/p>\n

                                                2002<\/h2>\n
                                                  \n
                                                1. J.-P. Vert, \u00ab\u00a0A tree kernel to analyze phylogenetic profiles\u00a0\u00bb, Bioinformatics<\/em>, vol. 18, p. S276-S284, 2002. [link]<\/a><\/li>\n
                                                2. J.-P. Vert, \u00ab\u00a0Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings\u00a0\u00bb, Proceedings of the Pacific Symposium on Biocomputing 2002<\/em>, Altman, R.B., Dunker, A.K., Hunter, L., Lauerdale, K. and Klein, T.E., (Ed.), World Scientific, pp. 649-660, 2002. [pdf]<\/a><\/li>\n<\/ol>\n\t\t<\/div>\n\t<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":385,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-311","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nPublications - CBIO<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.minesparis.psl.eu\/cbio\/publications\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Publications - CBIO\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.minesparis.psl.eu\/cbio\/publications\/\" \/>\n<meta property=\"og:site_name\" content=\"CBIO\" \/>\n<meta property=\"article:modified_time\" content=\"2025-08-14T15:58:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.minesparis.psl.eu\/cbio\/wp-content\/uploads\/sites\/24\/2025\/05\/CBIO-MINES-PARIS-PSL-1-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"901\" \/>\n\t<meta property=\"og:image:height\" content=\"418\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.minesparis.psl.eu\/cbio\/publications\/\",\"url\":\"https:\/\/www.minesparis.psl.eu\/cbio\/publications\/\",\"name\":\"Publications - 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