Search Results for author: Marie-Pierre Dubé

Found 2 papers, 1 papers with code

Deep interpretability for GWAS

no code implementations3 Jul 2020 Deepak Sharma, Audrey Durand, Marc-André Legault, Louis-Philippe Lemieux Perreault, Audrey Lemaçon, Marie-Pierre Dubé, Joelle Pineau

Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases.

Diet Networks: Thin Parameters for Fat Genomics

5 code implementations28 Nov 2016 Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, Yoshua Bengio

It is based on the idea that we can first learn or provide a distributed representation for each input feature (e. g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units).

Parameter Prediction

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