| 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 | 361 | 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 | 225 | 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 | 131 | 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 | 119 | 2015 |
| 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 | 92 | 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 | 86 | 2007 |
| Machine learning and genomics: precision medicine versus patient privacy CA Azencott Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2018 | 82 | 2018 |
| 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 | 80 | 2013 |
| 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 | 74 | 2019 |
| 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 | 67 | 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 | 54 | 2012 |
| Introduction au Machine Learning-2e éd. CA Azencott Dunod, 2022 | 42 | 2022 |
| 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 | 35 | 2023 |
| 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 |
| Efficient multi-task chemogenomics for drug specificity prediction B Playe, CA Azencott, V Stoven PloS one 13 (10), e0204999, 2018 | 16 | 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 | 13 | 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 |
| 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 | 12 | 2021 |
| kernelPSI: a post-selection inference framework for nonlinear variable selection L Slim, C Chatelain, CA Azencott, JP Vert International Conference on Machine Learning, 5857-5865, 2019 | 10 | 2019 |
| Multitask feature selection with task descriptors V Bellon, V Stoven, CA Azencott Biocomputing 2016: Proceedings of the Pacific Symposium, 261-272, 2016 | 9 | 2016 |
