Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling

no code implementations • 28 May 2019 • Emmanuel Noutahi, Dominique Beaini, Julien Horwood, Sébastien Giguère, Prudencio Tossou

We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs.

Drug Discovery Graph Representation Learning +3

Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance

no code implementations • 22 May 2015 • Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil

The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers.

On the String Kernel Pre-Image Problem with Applications in Drug Discovery

no code implementations • 3 Dec 2014 • Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand

This work uses a recent result on combinatorial optimization of linear predictors based on string kernels to develop, for the pre-image, a low complexity upper bound valid for many string kernels.

Combinatorial Optimization Drug Discovery +1

Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine

no code implementations • 2 Dec 2014 • Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil

The increased affordability of whole genome sequencing has motivated its use for phenotypic studies.