The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity DG Grimm, CA Azencott, F Aicheler, U Gieraths, DG MacArthur, ... Human mutation 36 (5), 513-523, 2015 | 394 | 2015 |
Learning to predict chemical reactions MA Kayala, CA Azencott, JH Chen, P Baldi Journal of chemical information and modeling 51 (9), 2209-2222, 2011 | 233 | 2011 |
A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval SJ Swamidass, CA Azencott, K Daily, P Baldi Bioinformatics 26 (10), 1348-1356, 2010 | 137 | 2010 |
Prediction of human population responses to toxic compounds by a collaborative competition F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ... Nature biotechnology 33 (9), 933-940, 2015 | 121 | 2015 |
Machine learning and genomics: precision medicine versus patient privacy CA Azencott Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2018 | 96 | 2018 |
Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis SK Sieberts, F Zhu, J García-García, E Stahl, A Pratap, G Pandey, ... Nature communications 7 (1), 12460, 2016 | 96 | 2016 |
One-to four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties CA Azencott, A Ksikes, SJ Swamidass, JH Chen, L Ralaivola, P Baldi Journal of chemical information and modeling 47 (3), 965-974, 2007 | 87 | 2007 |
Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data H Climente-González, CA Azencott, S Kaski, M Yamada Bioinformatics 35 (14), i427-i435, 2019 | 86 | 2019 |
Efficient network-guided multi-locus association mapping with graph cuts CA Azencott, D Grimm, M Sugiyama, Y Kawahara, KM Borgwardt Bioinformatics 29 (13), i171-i179, 2013 | 81 | 2013 |
Influence relevance voting: an accurate and interpretable virtual high throughput screening method SJ Swamidass, CA Azencott, TW Lin, H Gramajo, SC Tsai, P Baldi Journal of chemical information and modeling 49 (4), 756-766, 2009 | 68 | 2009 |
GLIDE: GPU-based linear regression for detection of epistasis T Kam-Thong, CA Azencott, L Cayton, B Pütz, A Altmann, N Karbalai, ... Human heredity 73 (4), 220-236, 2012 | 55 | 2012 |
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods A Behdenna, M Colange, J Haziza, A Gema, G Appé, CA Azencott, ... BMC bioinformatics 24 (1), 459, 2023 | 54 | 2023 |
Introduction au Machine Learning-2e éd. CA Azencott Dunod, 2022 | 52 | 2022 |
Multi-task feature selection on multiple networks via maximum flows M Sugiyama, CA Azencott, D Grimm, Y Kawahara, KM Borgwardt Proceedings of the 2014 SIAM International Conference on Data Mining, 199-207, 2014 | 19 | 2014 |
Drug target identification with machine learning: How to choose negative examples M Najm, CA Azencott, B Playe, V Stoven International journal of molecular sciences 22 (10), 5118, 2021 | 18 | 2021 |
Efficient multi-task chemogenomics for drug specificity prediction B Playe, CA Azencott, V Stoven PloS one 13 (10), e0204999, 2018 | 18 | 2018 |
Novel methods for epistasis detection in genome-wide association studies L Slim, C Chatelain, CA Azencott, JP Vert PLoS One 15 (11), e0242927, 2020 | 16 | 2020 |
The inconvenience of data of convenience: computational research beyond post-mortem analyses CA Azencott, T Aittokallio, S Roy, T Norman, S Friend, G Stolovitzky, ... Nature methods 14 (10), 937-938, 2017 | 13 | 2017 |
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 … E Dumas, L Laot, F Coussy, B Grandal Rejo, E Daoud, E Laas, A Kassara, ... Cancers 14 (11), 2671, 2022 | 12 | 2022 |
Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer H Climente-González, C Lonjou, F Lesueur, GENESIS Study Group, ... PLoS computational biology 17 (3), e1008819, 2021 | 10 | 2021 |