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Chloe Agathe Azencott

Chloe Agathe Azencott

Professor

Center · CBIO

Topic(s)
Genome and Cancer, Personalized and Predictive Medicine

Keywords

Awards & distinctions

  • 2021 Young Female Artificial Intelligence Engineer Award, organized by the Tilder agency in partnership with France Digitale and Challenges.

Team

CBIO

Biography

Chloé Agathe Azencott is a researcher specializing in bioinformatics and machine learning applied to genomics and precision medicine. Her work focuses on developing computational methods for analyzing complex biomedical data, particularly in the context of genome-wide association studies (GWAS) or large genomic data or the prediction of drug-target interactions. Her expertise includes the integration of prior biological knowledge, such as gene interaction networks, to enhance the robustness and interpretability of predictive models. Throughout her research, she has contributed to methodological advances in the detection of epistasis, feature selection, and multi-task machine learning, while addressing challenges related to data sparsity and high dimensionality.

Publication(s)

Teaching

Data Science

Course Director

Statistical concepts (population, estimation); dimensionality reduction; best practices (visualization, representativeness, confidentiality, algorithmic bias); supervised learning (empirical risk minimization, regularization, feature selection, and validation).

Introduction to machine learning

Course Director

In the age of data, various domains (Internet, marketing, logistics, biology, etc.) are accummulating enormous amounts of data, giving rise to the need for growing needs for automated tools that are able to exploit data of various nature. In particular, machine learning algorithms such as artificial neural networks, support vector machines or random forests can provide more powerful modeling and analyzes than classical linear statistical methods. This course aims at providing an overview of these algorithms, as well as their theoretical and methodological framework, through a diversity of applications.

Engineering and Health Research (Research Quarter)

Lecturer

PhD supervision

  • 2025 Biologically interpretable prediction of long-term disease risk ELGOHARY Kareem
  • 2025 Machine learning in bioinformatics: development of methods for transcriptomic data analysis AYADI Youmna
  • 2023 Analysis of the statistical properties of bacterial genomes to discern factors promoting gene exchange and migration events ETHEIMER Paul
  • 2022 Identification of biomarkers from transcriptomic data using the knockoff method: application to oncology cohorts CARTIER Julie
  • 2022 Machine learning and systems biology to identify therapeutic strategies in atip3-deficient triple-negative breast cancer GUICHAOUA Gwenn
  • 2019 Stable variable selection for genome-wide association studies NOUIRA Asma
  • 2019 Multimodal data learning to improve breast cancer treatment MBAYE Ndèye Mbaye
  • 2016 Detection of epistasis in genome-wide association studies using machine learning techniques for therapeutic target identification SLIM Lotfi
  • 2016 Genome-wide association studies guided by networks CLIMENTE GONZÁLEZ Héctor