Pierre Mahé
Pierre Mahé
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Graph kernels based on tree patterns for molecules
P Mahé, JP Vert
Machine learning 75 (1), 3-35, 2009
Extensions of marginalized graph kernels
P Mahé, N Ueda, T Akutsu, JL Perret, JP Vert
Proceedings of the twenty-first international conference on Machine learning, 70, 2004
Graph kernels for molecular structure− activity relationship analysis with support vector machines
P Mahé, N Ueda, T Akutsu, JL Perret, JP Vert
Journal of chemical information and modeling 45 (4), 939-951, 2005
A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events
M Jaillard, L Lima, M Tournoud, P Mahé, A Van Belkum, V Lacroix, ...
PLoS genetics 14 (11), e1007758, 2018
The pharmacophore kernel for virtual screening with support vector machines
P Mahé, L Ralaivola, V Stoven, JP Vert
Journal of Chemical Information and Modeling 46 (5), 2003-2014, 2006
Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum
P Mahe, M Arsac, S Chatellier, V Monnin, N Perrot, S Mailler, V Girard, ...
Bioinformatics 30 (9), 1280-1286, 2014
Large-scale machine learning for metagenomics sequence classification
K Vervier, P Mahé, M Tournoud, JB Veyrieras, JP Vert
Bioinformatics 32 (7), 1023-1032, 2016
Method for computing similarity between text spans using factored word sequence kernels
N Cancedda, P Mahé
US Patent 8,077,984, 2011
Digital antimicrobial susceptibility testing using the MilliDrop technology
L Jiang, L Boitard, P Broyer, AC Chareire, P Bourne-Branchu, P Mahé, ...
European Journal of Clinical Microbiology & Infectious Diseases 35, 415-422, 2016
Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection
P Mahé, M Tournoud
BMC bioinformatics 19, 1-11, 2018
Virtual screening with support vector machines and structure kernels
P Mahé, JP Vert
Combinatorial Chemistry & High Throughput Screening 12 (4), 409-423, 2009
A large scale evaluation of TBProfiler and Mykrobe for antibiotic resistance prediction in Mycobacterium tuberculosis
P Mahé, M El Azami, P Barlas, M Tournoud
PeerJ 7, e6857, 2019
Classification of proteomic MS data as Bayesian solution of an inverse problem
P Szacherski, JF Giovannelli, L Gerfault, P Mahé, JP Charrier, A Giremus, ...
IEEE Access 2, 1248-1262, 2014
Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data
K Vervier, P Mahé, JB Veyrieras, JP Vert
arXiv preprint arXiv:1506.07251, 2015
Factored sequence kernels
N Cancedda, P Mahé
Neurocomputing 72 (7-9), 1407-1413, 2009
Kernel design for virtual screening of small molecules with support vector machines
P Mahe
PhD thesis, Ecole des Mines de Paris, 2006
Identification Of Microorganisms By Spectrometry And Structured Classification
K Vervier, P Mahe, JB Veyrieras
US Patent App. 14/387,777, 2015
MetaVW: Large-scale machine learning for metagenomics sequence classification
K Vervier, P Mahé, JP Vert
Data Mining for Systems Biology: Methods and Protocols, 9-20, 2018
Interpreting k-mer–based signatures for antibiotic resistance prediction
M Jaillard, M Palmieri, A van Belkum, P Mahe
GigaScience 9 (10), giaa110, 2020
On learning matrices with orthogonal columns or disjoint supports
K Vervier, P Mahé, A d’Aspremont, JB Veyrieras, JP Vert
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014
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