Research interests

I focus on the development of methods for efficient multi-locus biomarker discovery. Essentially, my goal is to make sense of data with a small number of samples and a large number of variables. These variables can be clinical variables (such as age, cholesterol levels or smoking history) or genetic variables (such as gene expression, mutations, or epigenetic markers). How can we find out which of them play a role in a particular biological process or pathology? My work has numerous applications, in particular in precision medicine, where we try to develop treatments that are adapted to the (genetic) characteristics of patients, by contrast with a classical one-size-fits-all approach.

I am interested in the incorporation of additional (structured) information, for example as biological networks; in multi-task approaches, where one addresses multiple related problems simultaneously; and in the development of fast but accurate techniques to address these issues. In terms of machine learning, a lot of my work is linked to structured sparsity This has led for example to the development of SConES (Selecting CONected Explanatory SNPs), a method for network-guided multi-locus association mapping based on graph cuts.

I am also currently working on various projects involving the analysis of various types of biological networks, differential privacy, the integration of different data types, and the prediction of molecule-protein interactions.

Ongoing funded projects

  • PRAIRIE Chair
  • MLFPM: Machine Learning Frontiers in Precision Medicine. H2020 Innovative Training Network, 2019 – 2022.
  • SCAPHE: Methods for discovering SNP Combinations Associated with a PHEnotype from genome­wide data. ANR JCJC, 2019 – 2021.
  • Training distributed models. Collaboration with SANCARE, 2018 – 2020.
  • Machine learning for genome-wide association studies. Collaboration with SANOFI, 2016 – 2019.



  • Chloé-Agathe Azencott. Introduction au Machine Learning. Collection InfoSup, Dunod, 2018. EAN: 9782100780808

Preprint (1)

  • Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, Jean-Philippe Vert. Novel methods for epistasis detection in genome-wide association studies BioRXiv (2018)

Journal Papers (13)

  • Héctor Climente-González, Chloé-Agathe Azencott, Samuel Kaski and Makoto Yamada. Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data Bioinformatics, 2019, 35(14) i427–i435 doi:10.1093/bioinformatics/btz333, (ISMB/ECCB Proceedings, Open Access).
  • Benoît Playe, Chloé-Agathe Azencott, and Véronique Stoven. Efficient multi-task chemogenomics for drug specificity prediction. PLoS ONE, 2018, 13(10): e0204999 doi: 10.1371/journal.pone.0204999
  • Chloé-Agathe Azencott, Tero Aittokallio, Sushmita Roy, Thea Norman, Stephen Friend, Gustavo Stolovitzky, Anna Goldenberg, and DREAM Idea Challenge Consortium. The inconvenience of data of convenience: computational research beyond post-mortem analyses. Nature Methods, 2017, 14(10): 937-938. doi:10.1038/nmeth.4457 SharedIt [pdf]
  • Solveig K. Sieberts, Fan Zhu, Javier García-García, Eli Stahl, Abhishek Pratap, Gaurav Pandey, ..., Lara M. Mangravite. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis, Nature Communications, 2016, 7 doi:10.1038/ncomms12460 (Open Access)
  • Federica Eduati, Lara M Mangravite, Tao Wang, Hao Tang, J. Christopher Bare, Ruili Huang, ..., Julio Saez-Rodriguez. Prediction of human population responses to toxic compounds by a collaborative competition, Nature Biotechnology, 2015 33(9): 933—940 doi: 10.1038/nbt.3299 (Open Access)
  • Dominik G. Grimm, Chloé-Agathe Azencott, Fabian Aicheler, Udo Gieraths, Daniel G. MacArhur, Kaitlin E. Samocha, David N. Cooper, Peter D. Stenson, Mark J. Daly, Jordan W. Smoller, Laramie E. Duncan, Karsten M. Borgwardt. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity, Human Mutation, 2015, 36(5):513—523
    doi: 10.1002/humu.22768 (Open Access)
  • Chloé-Agathe Azencott, Dominik Grimm, Mahito Sugiyama, Yoshinobu Kawahara, and Karsten M. Borgwardt. Efficient network-guided multi-locus association mapping with graph cuts, Bioinformatics, 2013, 29(13): i171—i179 DOI: 10.1093/bioinformatics/btt238 (ISMB/ECCB Proceedings, Open Access) [pdf]
  • Tony Kam-Thong*, Chloé-Agathe Azencott*, Lawrence Cayton, Benno Pütz, André Altmann, Nazanin Karbalai, Philipp G. Sämann, Bernhard Schölkopf, Betram Müller-Myhsok, and Karsten M. Borgwardt. GLIDE: GPU-based linear regression for the detection of epistasis. Human Heredity, 2012, 73:220—236. DOI: 10.1159/000341885 [pdf]
  • Matthew A. Kayala, Chloé-Agathe Azencott, Jonathan H. Chen, and Pierre Baldi. Learning to predict chemical reactions, J. Chem. Inf. Model., 2011, 51(9):2209—2222. DOI: 10.1021/ci200207y [pdf]
  • S. Joshua Swamidass, Chloé-Agathe Azencott, Kenny Daily, and Pierre Baldi. A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval, Bioinformatics, 2010, 26(10):1348—1356.
    DOI: 10.1093/bioinformatics/btq140 [pdf]
  • S. Joshua Swamidass, Chloé-Agathe Azencott, Ting-Wan Lin, Hugo Gramajo, Sheryl Tsai, and Pierre Baldi. The Influence Relevance Voter: an accurate and interpretable virtual High throughput screening method, J. Chem. Inf. Model., 2009, 49(4):756—766. DOI: 10.1021/ci8004379 [pdf]
  • Chloé-Agathe Azencott, Alexandre Ksikes, S. Joshua Swamidass, Jonathan H. Chen, Liva Ralaivola, and Pierre Baldi. One- to four-dimensional kernels for virtual screening and the prediction of physical, chemical and biological properties, J. Chem. Inf. Model., 2007 , 47(3) pp 965—974
    DOI: 10.1021/ci600397p [pdf]

Conference Proceedings (4)

  • Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, and Jean-Philippe Vert. kernelPSI: a post-selection inference framework for nonlinear variable selection, Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML), 2019 97:5857—5865. [PMLR]
  • Víctor Bellón, Véronique Stoven, and Chloé-Agathe Azencott. Multitask feature selection with task descriptors, Pacific Symposium on Biocomputing, 2016 21: 261—272 [pdf]
  • Mahito Sugiyama, Chloé-Agathe Azencott, Dominik Grimm, Yoshinobu Kawahara, and Karsten M. Borgwardt. Multi-task feature selection on multiple networks via maximum flows, Proceedings of the 2014 SIAM International Conference on Data Mining, 2014
    doi: 10.1137/1.9781611973440.23 [pdf] [supplementary pdf]
  • Pierre Baldi, Chloé-Agathe Azencott, and S. Joshua Swamidass. Bridging the gap between neural network and kernel methods: applications to drug discovery, 20th Italian Workshop on Neural Nets, 2011 3—13 [pdf]

Book Chapters (2)

  • Chloé-Agathe Azencott and Pierre Baldi. Virtual high-throughput screening with two-dimensional kernels, in Hands-On Pattern Recognition: Challenges in Machine Learning, 1 pp 131—146, I. Guyon, G. Cawley, G. Dror, and A. Saffari Editors , Microtome, 2011, ISBN-13: 9780971977716 [pdf]

Monographs (2)

  • Chloé-Agathe Azencott. Machine learning for biomarker discovery, Sorbonne Université, HDR dissertation, 2019. tel-02354924.
  • Chloé-Agathe Azencott. Statistical machine learning and data mining for chemoinformatics and drug discovery, PhD dissertation, University of California, Irvine. ProQuest/UMI, 2010. AAT 3422105. [pdf].

Conference Abstracts (16)

  • Stefani Dritsa, Thibaud Martinez, Weiyi Zhang, Chloé-Agathe Azencott and Antonio Rausell. Prediction of candidate disease genes through deep learning on multiplex biological networks. JOBIM, 2019 (poster)
  • Christophe Le Priol, Chloé-Agathe Azencott and Xavier Gidrol. Large-scale RNA-seq datasets enable the detection of genes with a differential expression dispersion in cancer. JOBIM, 2019 (poster)
  • Diane Duroux*, Héctor Climente-González*, Aldo Camargo, Lars Wienbrandt, David Ellinghaus, Chloé-Agathe Azencott and Kristel Van Steen. Improving efficiency in epistasis detection with a gene-based analysis using functional filters., 28th International Genetic Epidemiology Society meeting, 2019.
  • Héctor Climente-González, Christine Lonjou, Fabienne Lesueur, Dominique Stoppa-Lyonnet, Nadine Andrieu, Chloé-Agathe Azencott, GENESIS investigators. Judging genetic loci by the company they keep: Comparing network-based methods for biomarker discovery in familial breast cancer., 68th Annual Meeting of the American Society of Human Genetics, 2018 (poster)
  • Héctor Climente-González and Chloé-Agathe Azencott, R package for network-guided Genome-Wide Association Studies, ISMB NetBio, 2017 (poster).
  • Christophe Le Priol, Laurent Guyon, Chloé-Agathe Azencott, and Xavier Gidrol. Analysis of microRNA sequences identifies conserved families of microRNAs, JOBIM, 2016 (poster).
  • Chloé-Agathe Azencott, S. Joshua Swamidass and Pierre Baldi. Virtual high-throughput screening and early recognition, Women in Machine Learning Workshop, 2009 (poster).
  • Chloé-Agathe Azencott, S. Joshua Swamidass and Pierre Baldi. Virtual high-throughput screening and early recognition, The Learning Workshop, 2009 (poster).
  • Chloé-Agathe Azencott and Pierre Baldi. Kernels for predictive regression--physical, biological and chemical properties of small molecules, Workshop for Women in Machine Learning, 2007 (oral).