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Cantin Baron

As seen in: BioRxiv
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Articles

Insights into Heart Failure Metabolite Markers through Explainable Machine Learning

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By Cantin Baron, Pamela Mehanna, Caroline Daneault, Leslie Hausermann
| BioRxiv
Abstract Understanding molecular traits through metabolomics offers an avenue to tailor cardiovascular prevention, diagnosis and treatment strategies more effectively. This study focuses on the application of machine learning (ML) and explainable artificial intelligence (XAI) algorithms to detect discriminant molecular signatures in heart failure (HF).
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Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

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By Cantin Baron, Sarah Cherkaoui, Sandra Therrien-Laperriere, Yann Ilboudo
| BioRxiv
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
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