Portrait of Guillaume Huguet is unavailable

Guillaume Huguet

guillaume.huguet@mila.quebec
Collaborating Alumni - Université de Montréal
Supervisor
Guy Wolf
Research Topics
Computational Biology
Generative Models
Spectral Learning
Google Scholar
GitHub

Publications

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover’s Distance
Alexander Tong
Guillaume Huguet
Dennis Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover’s distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an L(1) space, whose metric we call unbalanced diffusion earth mover’s distance (UDEMD). Next, we show how this gives distances between graph signals that are robust to noise. Finally, we apply this to organizing patients based on clinical notes, embedding cells modeled as signals on a gene graph, and organizing genes modeled as signals over a large cell graph. In each case, we show that UDEMD-based embeddings find accurate distances that are highly efficient compared to other methods.
2022-04-26
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)
doi.org
Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an
2021-12-31
IEEE International Conference on Acoustics, Speech and Signal Processing (unknown)
doi.org
arxiv.org
THE EFFECT SIZE OF GENES ON COGNITIVE ABILITIES IS LINKED TO THEIR EXPRESSION ALONG THE MAJOR HIERARCHICAL GRADIENT IN THE HUMAN BRAIN
Sébastien Jacquemont
Guillaume Huguet
Élise Douard
Zohra Saci
Guillaume Dumas
Laura Almasy
David C. Glahn
2021-10-30
European Neuropsychopharmacology (published)
doi.org
Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an
2021-07-25
arXiv (preprint)
doi.org
arxiv.org
Diffusion Earth Mover's Distance and Distribution Embeddings
Alexander Tong
Guillaume Huguet
Amine Natik
Kincaid MacDonald
Manik Kuchroo
Ronald R. Coifman
Guy Wolf
Smita Krishnaswamy
We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mo… (see more)ver's Distance (EMD). We model the datasets as distributions supported on common data graph that is derived from the affinity matrix computed on the combined data. In such cases where the graph is a discretization of an underlying Riemannian closed manifold, we prove that Diffusion EMD is topologically equivalent to the standard EMD with a geodesic ground distance. Diffusion EMD can be computed in
2021-07-19
International Conference on Machine Learning (Spotlight)
doi.org
proceedings.mlr.press
Topological Analysis of Single-Cell Hierarchy Reveals Inflammatory Glial Landscape of Macular Degeneration
Manik Kuchroo
Marcello DiStasio
Eric Song
Eda Calapkulu
Le Zhang
Maryam Ige
Amar H. Sheth
Madhvi Menon
Alexander Tong
Abhinav Godavarthi
Yu Xing
Scott Gigante
Holly Steach
Jessie Huang
Guillaume Huguet
Janhavi Narain
George Mourgkos
Rahul M. Dhodapkar
Matthew J. Hirn
Bastian Rieck … (see 3 more)
Guy Wolf
Smita Krishnaswamy
Brian P. Hafler
2020-12-31
SSRN Electronic Journal (unknown)
doi.org
Topological Analysis of Single-Cell Hierarchy Reveals Inflammatory Glial Landscape of Macular Degeneration
Manik Kuchroo
Marcello DiStasio
Eric Song
Eda Calapkulu
Le Zhang
Maryam Ige
Amar H. Sheth
Madhvi Menon
Alexander Tong
Abhinav Godavarthi
Yu Xing
Scott Gigante
Holly Steach
Jessie Huang
Guillaume Huguet
Janhavi Narain
George Mourgkos
Rahul M. Dhodapkar
Matthew J. Hirn
Bastian Rieck … (see 3 more)
Guy Wolf
Smita Krishnaswamy
Brian P. Hafler
2020-12-31
SSRN Electronic Journal (published)
doi.org
General Principles of Gene Dosage Effects on Brain Structure
Claudia Modenato
Kuldeep Kumar
Clara A. Moreau
Catherine Schramm
Guillaume Huguet
Sandra Martin-Brevet
Aurélie Pain
Anne M. Maillard
Sonia Richetin
Borja Rodriguez-Herreros
Lester Melie-Garcia
Ana Dos Santos Silva
Marianne B.M. van den Bree
David E.J. Linden
Carrie E. Bearden
Danilo Bzdok
Sarah Lippé
Mallar Chakravarty
Bogdan Draganski
Sébastien Jacquemont
2020-04-30
Biological Psychiatry (published)
doi.org
Neuropsychiatric copy number variants exert shared effects on human brain structure
Claudia Modenato
Kuldeep Kumar
Clara Moreau
Sandra Martin‐Brevet
Guillaume Huguet
Catherine Schramm
Martineau Jean‐Louis
Charles-Olivier Martin
Nadine Younis
Petra Tamer
Élise Douard
Fanny Thébault‐Dagher
Valérie Côté
Audrey Charlebois
Florence Deguire
Anne Maillard
Borja Rodríguez‐Herreros
Aurélie Pain
Sonia Richetin
Leila Kushan … (see 12 more)
Lester Melie‐García
Ana Isabel Silva
Marianne van den Bree
David E.J. Linden
Michael J. Owen
Jeremy Hall
Sarah Lippé
M. Mallar Chakravarty
Danilo Bzdok
Carrie E. Bearden
Bogdan Draganski
Sébastien Jacquemont
Neuropsychiatric CNVs share neuroanatomical signatures characterized by a parsimonious set of brain dimensions. The latter may underlie the … (see more)risk conferred by CNVs for a similar spectrum of neuropsychiatric conditions.
2020-04-16
medRxiv (preprint)
doi.org
Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia
Clara A. Moreau
Sebastian G. W. Urchs
Pierre Orban
Catherine Schramm
Guillaume Dumas
Aurélie Labbe
Guillaume Huguet
Élise Douard
Pierre-Olivier Quirion
Amy Lin
Leila Kushan
Stephanie Grot
David Luck
Adrianna Mendrek
Stephane Potvin
Emmanuel Stip
Thomas Bourgeron
Alan C. Evans
Carrie E. Bearden
Pierre Bellec … (see 1 more)
Sébastien Jacquemont
16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Defic… (see more)it-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. Here we report an analysis of resting-state FC using magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We characterize CNV FC-signatures and use them to identify dimensions contributing to complex idiopathic conditions. CNVs have large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions play a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibit worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.
2019-12-05
bioRxiv (preprint)
doi.org