742 citations · 806 across the 9 of their papers we have counts for
9 papers
Invariant Causal Set Covering Machines
Thibaud Godon, Baptiste Bauvin, Pascal Germain +2
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable…
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin +3
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges l…
Large scale modeling of antimicrobial resistance with interpretable classifiers
Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre +3
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome…
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
Alexandre Drouin, Sébastien Giguère, Maxime Déraspe +3
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole gen…
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich +4
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes…
Improved design and screening of high bioactivity peptides for drug discovery
Sébastien Giguère, François Laviolette, Mario Marchand +4
The discovery of peptides having high biological activity is very challenging mainly because there is an enormous diversity of compounds and only a minority have the desired proper…
Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species
Keith R. Bradnam, Joseph N. Fass, Anton Alexandrov +88
Background - The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished g…
Learning a peptide-protein binding affinity predictor with kernel ridge regression
Sébastien Giguère, Mario Marchand, François Laviolette +2
We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino ac…
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
Mohak Shah, Mario Marchand, Jacques Corbeil
One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance…