Chloé-Agathe Azencott
Chloé-Agathe Azencott
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Zitiert von
Zitiert von
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
Learning to predict chemical reactions
MA Kayala, CA Azencott, JH Chen, P Baldi
Journal of chemical information and modeling 51 (9), 2209-2222, 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
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
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
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
Machine learning and genomics: precision medicine versus patient privacy
CA Azencott
Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 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
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
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
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
Introduction au Machine Learning-2e éd.
CA Azencott
Dunod, 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
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
Efficient multi-task chemogenomics for drug specificity prediction
B Playe, CA Azencott, V Stoven
PloS one 13 (10), e0204999, 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
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
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
Multitask feature selection with task descriptors
V Bellon, V Stoven, CA Azencott
Biocomputing 2016: Proceedings of the Pacific Symposium, 261-272, 2016
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
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