Ongoing projects

  • 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


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

Journal Papers (12)

  • 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 (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 (3)

  • 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]

Doctoral Dissertation (1)

  • Chloé-Agathe Azencott. Statistical Machine Learning and Data Mining for Chemoinformatics and Drug Discovery, University of California, Irvine. ProQuest/UMI, 2010. AAT 3422105. [pdf]

Conference Abstracts (12)

  • 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)
  • 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).