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Chloé-Agathe Azencott
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2020 – today
- 2024
- [j11]Gwenn Guichaoua, Philippe Pinel, Brice Hoffmann, Chloé-Agathe Azencott, Véronique Stoven:
Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset. J. Chem. Inf. Model. 64(18): 6938-6956 (2024) - 2023
- [j10]Abdelkader Behdenna, Maximilien Colange, Julien Haziza, Aryo Gema, Guillaume Appé, Chloé-Agathe Azencott, Akpéli Nordor:
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinform. 24(1): 459 (2023) - [j9]Christophe Le Priol, Chloé-Agathe Azencott, Xavier Gidrol:
Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression. PLoS Comput. Biol. 19(3) (2023) - 2022
- [j8]Veronica Tozzo, Chloé-Agathe Azencott, Samuele Fiorini, Emanuele Fava, Andrea Trucco, Annalisa Barla:
Where Do We Stand in Regularization for Life Science Studies? J. Comput. Biol. 29(3): 213-232 (2022) - [i3]Elise Dumas, Anne-Sophie Hamy, Sophie Houzard, Eva Hernandez, Aullène Toussaint, Julien Guerin, Laetitia Chanas, Victoire de Castelbajac, Mathilde Saint-Ghislain, Beatriz Grandal, Eric Daoud, Fabien Reyal, Chloé-Agathe Azencott:
EDEN : An Event DEtection Network for the annotation of Breast Cancer recurrences in administrative claims data. CoRR abs/2211.08077 (2022) - 2021
- [j7]Héctor Climente-González, Christine Lonjou, Fabienne Lesueur, GENESIS study group, Dominique Stoppa-Lyonnet, Nadine Andrieu, Chloé-Agathe Azencott:
Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer. PLoS Comput. Biol. 17(3) (2021) - 2020
- [b1]Chloé-Agathe Azencott:
Machine learning tools for biomarker discovery. (Outils d'apprentissage statistique pour la découverte de biomarqueurs). Sorbonne University, France, 2020
2010 – 2019
- 2019
- [j6]Héctor Climente-González, Chloé-Agathe Azencott, Samuel Kaski, Makoto Yamada:
Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data. Bioinform. 35(14): i427-i435 (2019) - [c4]Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, Jean-Philippe Vert:
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection. ICML 2019: 5857-5865 - 2018
- [i2]Chloé-Agathe Azencott:
Machine learning and genomics: precision medicine vs. patient privacy. CoRR abs/1802.10568 (2018) - 2016
- [c3]Victor Bellon, Véronique Stoven, Chloé-Agathe Azencott:
Multitask Feature Selection with Task Descriptors. PSB 2016: 261-272 - [p1]Chloé-Agathe Azencott:
Network-Guided Biomarker Discovery. Machine Learning for Health Informatics 2016: 319-336 - [i1]Chloé-Agathe Azencott:
Network-Guided Biomarker Discovery. CoRR abs/1607.08161 (2016) - 2014
- [c2]Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt:
Multi-Task Feature Selection on Multiple Networks via Maximum Flows. SDM 2014: 199-207 - 2013
- [j5]Chloé-Agathe Azencott, Dominik G. Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt:
Efficient network-guided multi-locus association mapping with graph cuts. Bioinform. 29(13): 171-179 (2013) - 2011
- [j4]Matthew A. Kayala, Chloé-Agathe Azencott, Jonathan H. Chen, Pierre Baldi:
Learning to Predict Chemical Reactions. J. Chem. Inf. Model. 51(9): 2209-2222 (2011) - 2010
- [j3]Sanjay Joshua Swamidass, Chloé-Agathe Azencott, Kenny Daily, Pierre Baldi:
A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval. Bioinform. 26(10): 1348-1356 (2010) - [c1]Pierre Baldi, Chloé-Agathe Azencott, Sanjay Joshua Swamidass:
Bridging the Gap Between Neural Network and Kernel Methods: Applications to Drug Discovery. WIRN 2010: 3-13
2000 – 2009
- 2009
- [j2]S. Joshua Swamidass, Chloé-Agathe Azencott, Ting-Wan Lin, Hugo Gramajo, Shiou-Chuan Tsai, Pierre Baldi:
Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method. J. Chem. Inf. Model. 49(4): 756-766 (2009) - 2007
- [j1]Chloé-Agathe Azencott, Alexandre Ksikes, S. Joshua Swamidass, Jonathan H. Chen, Liva Ralaivola, Pierre Baldi:
One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties. J. Chem. Inf. Model. 47(3): 965-974 (2007)
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