Author

Pamela Mehanna

Montreal Heart InstituteORCID 0000-0002-4918-5291Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:metabolomics· 2 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Cantin BaroniD· 2 shared Christine Des RosiersiD· 2 shared Julie HussiniD· 2 shared Matthieu RuiziD· 2 shared Allison E. Ashley‐KochiD· 1 shared Caroline DaneaultiD· 1 shared David BusseuiliD· 1 shared Guillaume LettreiD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersBioinformatics

Cantin BaronORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Insights into Heart Failure Metabolite Markersthrough Explainable Machine Learning

Cantin Baron, Pamela Mehanna, Caroline Daneault, Leslie Hausermann, David Busseuil, Jean‐Claude Tardif, Jocelyn Dupuis, Christine Des Rosiers, Matthieu Ruiz, Julie Hussin

This study uses machine learning and explainable AI to identify metabolite markers for heart failure, finding that lignoceric acid is a significant discriminator.

Computational and Structural Biotechnology Journal · Jan 1, 2025Peer-reviewedBioinformatics

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Cantin Baron, Sarah Cherkaoui, Sandra Therrien-Laperriere, Yann Ilboudo, Raphaël Poujol, Pamela Mehanna, Melanie E. Garrett, Marilyn J. Telen, Allison E. Ashley‐Koch, Pablo Bartolucci, John David Rioux, Guillaume Lettre, Christine Des Rosiers, Matthieu Ruiz, Julie Hussin

This paper introduces the shortest reactional distance (SRD) metric to improve the biological interpretation of metabolite genome-wide association study (mGWAS) results by linking genes to metabolites based on metabolic pathway knowledge.

iScience · Nov 14, 2023Peer-reviewedSystems Biology