Publications

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

Cantin Baron

Sarah Cherkaoui

Sandra Therrien-Laperriere

Yann Ilboudo

Raphael Poujol

Pamela Mehanna

Melanie E. Garrett

Marilyn J. Telen

Allison E. Ashley-Koch

Pablo Bartolucci

John D. Rioux

Guillaume Lettre

Christine Des Rosiers

Matthieu Ruiz

Julie Hussin

Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights in… (voir plus)to our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.

2023-11-01

iScience (publié)

doi.org

Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.

Elham Emami

Samira Abbasgholizadeh-Rahimi

Milka Nyariro

Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (voir plus) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.

2023-11-01

Journal (published)

pubmed.ncbi.nlm.nih.gov

Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.

Elham Emami

Samira Abbasgholizadeh-Rahimi

Milka Nyariro

Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (voir plus) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.

2023-11-01

Journal (publié)

pubmed.ncbi.nlm.nih.gov

Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications

Felix Schoeller

Adam Haar Horowitz

Abhinandan Jain

Pattie Maes

Nicco Reggente

Leonardo Christov-Moore

Giovanni Pezzulo

Laura Barca

Micah Allen

Roy Salomon

Mark Miller

Daniele Di Lernia

Giuseppe Riva

Manos Tsakiris

Moussa A. Chalah

Arno Klein

Ben Zhang

Teresa Garcia

Ursula Pollack

Marion Trousselard … (voir 4 de plus)

Charles Verdonk

Guillaume Dumas

Vladimir Adrien

Karl J. Friston

2023-11-01

Neuroscience and Biobehavioral Reviews (publié)

doi.org

Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review.

Jordan J. Levett

Lior M. Elkaim

Farbod Niazi

Michael H. Weber

Christian Iorio-Morin

Marco Bonizzato

Alexander G. Weil

2023-11-01

Neuromodulation: Technology at the Neural Interface (publié)

doi.org

Investigation of the Dosimetry Characteristics of the GAFCHROMIC® EBT3 Film Response to Alpha Particle Irradiation

Mélodie Cyr

Victor D. Martinez

S. Devic

Nada Tomic

David F. Lewis

Shirin A. Enger

2023-11-01

Journal of Physics: Conference Series (publié)

doi.org

Lag-Llama: Towards Foundation Models for Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Biloš

Hena Ghonia

Nadhir Hassen

Anderson Schneider

Sahil Garg

Alexandre Drouin

Nicolas Chapados

Yuriy Nevmyvaka

Irina Rish

Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (voir plus)Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.

2023-11-01

NeurIPS.cc/2023/Workshop/R0-FoMo (poster)

doi.org

openreview.net

Mining Mass Spectra for Peptide Facts

Jeremie Zumer

Sébastien Lemieux

The current mainstream software for peptide-centric tandem mass spectrometry data analysis can be categorized as either database-driven, whi… (voir plus)ch rely on a library of mass spectra to identify the peptide associated with novel query spectra, or de novo sequencing-based, which aim to find the entire peptide sequence by relying only on the query mass spectrum. While the first paradigm currently produces state-of-the-art results in peptide identification tasks, it does not inherently make use of information present in the query mass spectrum itself to refine identifications. Meanwhile, de novo approaches attempt to solve a complex problem in one go, without any search space constraints in the general case, leading to comparatively poor results. In this paper, we decompose the de novo problem into putatively easier subproblems, and we show that peptide identification rates of database-driven methods may be improved in terms of peptide identification rate by solving one such subsproblem without requiring a solution for the complete de novo task. We demonstrate this using a de novo peptide length prediction task as the chosen subproblem. As a first prototype, we show that a deep learning-based length prediction model increases peptide identification rates in the ProteomeTools dataset as part of an Pepid-based identification pipeline. Using the predicted information to better rank the candidates, we show that combining ideas from the two paradigms produces clear benefits in this setting. We propose that the next generation of peptide-centric tandem mass spectrometry identification methods should combine elements of these paradigms by mining facts “de novo; about the peptide represented in a spectrum, while simultaneously limiting the search space with a peptide candidates database.

2023-11-01

bioRxiv (prépublication)

doi.org

Open design of a reproducible videogame controller for MRI and MEG

Yann Harel

André Cyr

Julie Boyle

Basile Pinsard

Jeremy Bernard

Marie-France Fourcade

Himanshu Aggarwal

Ana Fernanda Ponce