Mathieu Blanchette

Google Scholar

Yijie Zhang

PhD - McGill University

Google Scholar

Publications

ARGV: 3D genome structure exploration using augmented reality

Chrisostomos Drogaris

Yanlin Zhang

Éric Zhang

Elena Nazarova

Roman Sarrazin-Gendron

Sélik Wilhelm-Landry

Yan Cyr

Jacek Majewski

Jérôme Waldispühl

2024-08-27

BMC Bioinformatics (published)

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

Lazar Atanackovic

Xi Zhang

Brandon Amos

Leo J Lee

Alexander Tong

Kirill Neklyudov

Numerous biological and physical processes can be modeled as systems of interacting samples evolving continuously over time, e.g. the dynami… (see more)cs of communicating cells or physical particles. Flow-based models allow for learning these dynamics at the population level --- they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We propose

2024-06-17

ICML.cc/2024/Workshop/GRaM (published)

openreview.net

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

Lazar Atanackovic

Xi Zhang

Brandon Amos

Leo J Lee

Alexander Tong

Kirill Neklyudov

Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynam… (see more)ics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predicting the temporal evolution of populations across novel samples and unseen environments. Flow-based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. That is, the change of the population at any moment in time depends on the population itself due to the interactions between samples. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depends on the microenvironment of cells specific to each patient. We propose Meta Flow Matching (MFM), a practical approach to integrating along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations. Namely, we embed the population of samples using a Graph Neural Network (GNN) and use these embeddings to train a Flow Matching model. This gives MFM the ability to generalize over the initial distributions unlike previously proposed methods. We demonstrate the ability of MFM to improve prediction of individual treatment responses on a large scale multi-patient single-cell drug screen dataset.

2024-06-17

ICML.cc/2024/Workshop/GRaM (published)

openreview.net

Improving microbial phylogeny with citizen science within a mass-market video game

Roman Sarrazin-Gendron

Parham Ghasemloo Gheidari

Alexander Butyaev

Timothy Keding

Eddie Cai

Jiayue Zheng

Renata Mutalova

Julien Mounthanyvong

Yuxue Zhu

Elena Nazarova

Chrisostomos Drogaris

Kornél Erhart

David Bélanger

Amélie Brouillette

Michael Bouffard

Gabriel Richard

Joshua Davidson

Randy Pitchford

Mathieu Falaise

Sébastien Caisse … (see 14 more)

Vincent Fiset

Steven Hebert

Daniel McDonald

Dan Hewitt

Rob Knight

Jonathan Huot

Attila Szantner

Seung Kim

Jérôme Waldispühl

Jonathan Moreau-Genest

David Najjab

Steve Prince

Ludger Saintélien

2024-04-15

Nature Biotechnology (published)

Posterior inference of Hi-C contact frequency through sampling

Yanlin Zhang

Christopher J. F. Cameron

Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are r… (see more)epresented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.

2024-02-22

Frontiers in Bioinformatics (published)

Multi-ancestry polygenic risk scores using phylogenetic regularization

Elliot Layne

Shadi Zabad

Yue Li

2024-02-17

bioRxiv (preprint)

PERFUMES: pipeline to extract RNA functional motifs and exposed structures

Arnaud Chol

Roman Sarrazin-Gendron

Éric Lécuyer

Jérôme Waldispühl

Abstract Motivation Up to 75% of the human genome encodes RNAs. The function of many non-coding RNAs relies on their ability to fold into 3D… (see more) structures. Specifically, nucleotides inside secondary structure loops form non-canonical base pairs that help stabilize complex local 3D structures. These RNA 3D motifs can promote specific interactions with other molecules or serve as catalytic sites. Results We introduce PERFUMES, a computational pipeline to identify 3D motifs that can be associated with observable features. Given a set of RNA sequences with associated binary experimental measurements, PERFUMES searches for RNA 3D motifs using BayesPairing2 and extracts those that are over-represented in the set of positive sequences. It also conducts a thermodynamics analysis of the structural context that can support the interpretation of the predictions. We illustrate PERFUMES’ usage on the SNRPA protein binding site, for which the tool retrieved both previously known binder motifs and new ones. Availability and implementation PERFUMES is an open-source Python package (https://jwgitlab.cs.mcgill.ca/arnaud_chol/perfumes).

2024-01-30

Bioinform. (published)

Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments

Dongjoon Lim

Changhyun Baek

2024-01-26

iScience (published)

PhyloGFN: Phylogenetic inference with generative flow networks

Ming Yang Zhou

Zichao Yan

Elliot Layne

Nikolay Malkin

Dinghuai Zhang

Moksh J. Jain

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

Learning the Game: Decoding the Differences between Novice and Expert Players in a Citizen Science Game with Millions of Players

Eddie Cai

Roman Sarrazin-Gendron

Renata Mutalova

Parham Ghasemloo Gheidari

Alexander Butyaev

Gabriel Richard

Sébastien Caisse

Rob Knight

Attila Szantner

Jérôme Waldispühl

2024-01-01

FDG (published)

H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs

Brian Krug

Bo Hu

Haifen Chen

Adam Ptack

Xiao Chen

Kristjan H. Gretarsson

Shriya Deshmukh

Nisha Kabir

Augusto Faria Andrade

Elias Jabbour

Ashot S. Harutyunyan

John J. Y. Lee

Maud Hulswit

Damien Faury

Caterina Russo

Xinjing Xu

Michael Johnston

Audrey Baguette

Nathan A. Dahl

Alexander G. Weil … (see 12 more)

Benjamin Ellezam

Rola Dali

Khadija Wilson

Benjamin A. Garcia

Rajesh Kumar Soni

Marco Gallo

Michael D. Taylor

Claudia Kleinman

Jacek Majewski

Nada Jabado

Chao Lu

2023-11-28

bioRxiv (preprint)

Player-Guided AI outperforms standard AI in Sequence Alignment Puzzles

Renata Mutalova

Roman Sarrazin-Gendron

Parham Ghasemloo Gheidari

Eddie Cai

Gabriel Richard

Sébastien Caisse

Rob Knight

Attila Szantner

Jérôme Waldispühl

2023-11-05

International Conference on Climate Informatics (published)