| Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons A Drouin, S Giguère, M Déraspe, M Marchand, M Tyers, VG Loo, ... BMC genomics 17, 1-15, 2016 | 148 | 2016 |
| Machine learning assisted design of highly active peptides for drug discovery S Giguere, F Laviolette, M Marchand, D Tremblay, S Moineau, X Liang, ... PLoS computational biology 11 (4), e1004074, 2015 | 72 | 2015 |
| MHC-NP: predicting peptides naturally processed by the MHC S Giguère, A Drouin, A Lacoste, M Marchand, J Corbeil, F Laviolette Journal of immunological methods 400, 30-36, 2013 | 65 | 2013 |
| Learning a peptide-protein binding affinity predictor with kernel ridge regression S Giguere, M Marchand, F Laviolette, A Drouin, J Corbeil BMC bioinformatics 14, 1-16, 2013 | 55 | 2013 |
| Towards interpretable sparse graph representation learning with laplacian pooling E Noutahi, D Beaini, J Horwood, S Giguère, P Tossou arXiv preprint arXiv:1905.11577, 2019 | 45 | 2019 |
| Machine learning of global phosphoproteomic profiles enables discrimination of direct versus indirect kinase substrates E Kanshin, S Giguère, C Jing, M Tyers, P Thibault Molecular & Cellular Proteomics 16 (5), 786-798, 2017 | 39 | 2017 |
| Risk bounds and learning algorithms for the regression approach to structured output prediction S Giguère, F Laviolette, M Marchand, K Sylla International Conference on Machine Learning, 107-114, 2013 | 15 | 2013 |
| Learning interpretable models of phenotypes from whole genome sequences with the set covering machine A Drouin, S Giguere, V Sagatovich, M Déraspe, F Laviolette, M Marchand, ... arXiv preprint arXiv:1412.1074, 2014 | 12 | 2014 |
| Algorithms for the hard pre-image problem of string kernels and the general problem of string prediction S Giguere, A Rolland, F Laviolette, M Marchand International Conference on Machine Learning, 2021-2029, 2015 | 7 | 2015 |
| A pseudo-Boolean set covering machine P Germain, S Giguere, JF Roy, B Zirakiza, F Laviolette, CG Quimper Principles and Practice of Constraint Programming: 18th International …, 2012 | 4 | 2012 |
| Pac-bayesian risk bounds and learning algorithms for the regression approach to structured output prediction S Giguere, F Laviolette, M Marchand, A Rolland Advanced Structured Prediction 239, 2014 | 2 | 2014 |
| Set Covering Machines and Reference-Free Genome Comparisons Uncover Predictive Biomarkers of Antibiotic Resistance A Drouin, S Giguère, M Déraspe, M Marchand, J Corbeil, F Laviolette | | 2016 |
| Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance A Drouin, S Giguère, M Déraspe, F Laviolette, M Marchand, J Corbeil arXiv preprint arXiv:1505.06249, 2015 | | 2015 |
| Machine learning assisted design of highly active peptides for drug discovery D Tremblay, É Biron, S Giguère, S Moineau, F Laviolette, X Liang, ... Public Library of Science, 2015 | | 2015 |
| Algorithmes d'apprentissage automatique pour la conception de composés pharmaceutiques et de vaccins S Giguère Université Laval, 2015 | | 2015 |
| On the String Kernel Pre-Image Problem with Applications in Drug Discovery S Giguere, A Rolland, F Laviolette, M Marchand arXiv preprint arXiv:1412.1463, 2014 | | 2014 |
| Improved design and screening of high bioactivity peptides for drug discovery S Giguere, F Laviolette, M Marchand, D Tremblay, S Moineau, É Biron, ... arXiv preprint arXiv:1311.3573, 2013 | | 2013 |
| Entre le chaos primordial et l'émergence des êtres vivants: le problème du principe anthropique S Giguère National Library of Canada= Bibliothèque nationale du Canada, Ottawa, 2001 | | 2001 |
| Algorithmes d’apprentissage automatique pour la conception de composés pharmaceutiques et de vaccins Thèse S Giguère | | |
| ICML 2013–SUPPLEMENTARY MATERIAL S Giguere, F Laviolette, M Marchand, K Sylla | | |
Mario Marchandprofesseur d'informatique (retraité), Université LavalEmail yang diverifikasi di ift.ulaval.ca