Author

Lea A. Harrington

Institute for Research in Immunology and CancerORCID 0000-0002-4977-2744Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:CRISPR screen· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Pierre ThibaultiD· 2 shared Alain VerreaultiD· 1 shared Albert FeghalyiD· 1 shared Almer M. van der SlootiD· 1 shared Andrew Chatr‐aryamontriiD· 1 shared Anne MarinieriD· 1 shared Benjamin H. B. KwokiD· 1 shared Bobby‐Joe BreitkreutziD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersSystems Biology

Pierre ThibaultORCID

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2 papers

Recent·Most cited

A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

Jasmin Coulombe‐Huntington, Thierry Bertomeu, Caroline Huard, Andrew Chatr‐aryamontri, Daniel J. St‐Cyr, María Sánchez‐Osuna, David J. Papadopoli, Karine Normandin, Mohammadjavad Paydar, Shannon McLaughlan, Corinne St‐Denis, Li Zhang, Henry Say, Roger Palou, Chris Stark, , Mike Tyers

The paper presents a chemical-genetic interaction matrix derived from CRISPR/Cas9 screens, revealing drug mechanisms and genetic architecture in human cells.

bioRxiv · Jan 30, 2026 · PreprintSystems Biology

CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation

Tariq Daouda, Maude Dumont-Lagacé, Albert Feghaly, Yahya Benslimane, Rébecca Panès, Mathieu Courcelles, Mohamed Benhammadi, Lea A. Harrington, Pierre Thibault, François Major, Yoshua Bengio, Étienne Gagnon, Sébastien Lemieux, Claude Perreault

The paper introduces CAMAP, an artificial neural network that predicts MHC-I peptide presentation by analyzing codon arrangement in mRNA sequences, improving prediction accuracy beyond traditional methods.

bioRxiv · Jun 3, 2020 · PreprintBioinformatics