Search Results for author: Julie G. Hussin

Found 2 papers, 1 papers with code

The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology

no code implementations • 8 May 2024 • Erin E. Gill, Baofeng Jia, Carmen Lia Murall, Raphaël Poujol, Muhammad Zohaib Anwar, Nithu Sara John, Justin Richardsson, Ashley Hobb, Abayomi S. Olabode, Alexandru Lepsa, Ana T. Duggan, Andrea D. Tyler, Arnaud N'Guessan, Atul Kachru, Brandon Chan, Catherine Yoshida, Christina K. Yung, David Bujold, Dusan Andric, Edmund Su, Emma J. Griffiths, Gary Van Domselaar, Gordon W. Jolly, Heather K. E. Ward, Henrich Feher, Jared Baker, Jared T. Simpson, Jaser Uddin, Jiannis Ragoussis, Jon Eubank, Jörg H. Fritz, José Héctor Gálvez, Karen Fang, Kim Cullion, Leonardo Rivera, Linda Xiang, Matthew A. Croxen, Mitchell Shiell, Natalie Prystajecky, Pierre-Olivier Quirion, Rosita Bajari, Samantha Rich, Samira Mubareka, Sandrine Moreira, Scott Cain, Steven G. Sutcliffe, Susanne A. Kraemer, Yann Joly, Yelizar Alturmessov, CPHLN consortium, CanCOGeN consortium, VirusSeq Data Portal Academic, Health network, Marc Fiume, Terrance P. Snutch, Cindy Bell, Catalina Lopez-Correa, Julie G. Hussin, Jeffrey B. Joy, Caroline Colijn, Paul M. K. Gordon, William W. L. Hsiao, Art F. Y. Poon, Natalie C. Knox, Mélanie Courtot, Lincoln Stein, Sarah P. Otto, Guillaume Bourque, B. Jesse Shapiro, Fiona S. L. Brinkman

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response.

Epidemiology

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Diet Networks: Thin Parameters for Fat Genomics

5 code implementations • 28 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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