Portrait de Julie Hussin

Julie Hussin

Membre académique associé

Professeure adjointe, Université de Montréal

Biographie

Julie Hussin est professeure agrégée à la Faculté de médecine de l'Université de Montréal (UdeM) et chercheuse à l'Institut de cardiologie de Montréal (ICM). Elle est aussi chercheuse-boursière junior 2 du Fonds de recherche du Québec - Santé (FRQS) et responsable des programmes d'études supérieures en bio-informatique à l'UdeM.

Julie Hussin a été formée en génomique statistique et évolutive et possède une vaste expérience dans l'analyse de données multi-omiques issues de vastes cohortes populationnelles. Ses travaux en biologie computationnelle se concentrent principalement sur la génomique médicale et des populations, contribuant à plusieurs avancées méthodologiques dans ces domaines. Son travail interdisciplinaire vise à développer des outils novateurs pour la médecine de précision.

Ses projets de recherche se focalisent sur l'amélioration de la prédiction de risques et la gestion des maladies cardiométaboliques, en particulier dans le cas de l'insuffisance cardiaque. Les méthodologies utilisées dans son groupe intègrent différentes sources de données, notamment des données cliniques, génétiques, transcriptomiques, protéomiques et métabolomiques, pour permettre la découverte de nouvelles informations sur les déterminants biologiques des maladies cardiaques, notamment par des techniques d’apprentissage non supervisé. Dans le contexte de la pandémie de COVID-19, son équipe a également développé des approches d’analyse de données génétiques des virus, pour la surveillance virale et l’étude des interactions hôte-pathogène ainsi que l'évolution virale.

Ses intérêts de recherche comprennent également l'interprétabilité, la généralisation et l'équité des algorithmes d'apprentissage automatique dans la recherche en santé. Julie Hussin s'engage à promouvoir activement une IA équitable, sûre et transparente dans la recherche en santé et s'efforce d'assurer l'inclusivité et la représentativité des individus dans sa recherche, pour que son travail bénéficie à l'ensemble de la population. Elle partage son expertise en donnant plusieurs cours de bio-informatique et de génétique des populations, ainsi que d’apprentissage automatique en génomique. Avant de se joindre à l'Université de Montréal en tant que professeure, elle a été boursière postdoctorale du Human Frontier Science Program au Wellcome Trust Centre for Human Genetics de l'Université d'Oxford (Linacre College) et chercheuse postdoctorale invitée à l'Université McGill.

Étudiants actuels

Alexis Nolin-Lapalme

Doctorat - Université de Montréal

alexis.nolin-lapalme@mila.quebec

Camille Rochefort-Boulanger

Doctorat - Université de Montréal

Co-superviseur⋅e :

rochefoc@mila.quebec

Doctorat - Université de Montréal

cantin.baron@mila.quebec

Doctorat - Université de Montréal

david.hamelin@mila.quebec

Fatima Mostefai

Doctorat - Université de Montréal

fatima.mostefai@mila.quebec

Matthew Scicluna

Doctorat - Université de Montréal

Co-superviseur⋅e :

Sébastien Lemieux

matthew.scicluna@mila.quebec

Maîtrise recherche - Université de Montréal

simon.paquette@mila.quebec

Publications

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages

Fatima Mostefai

I. Gamache

Arnaud N’Guessan

Justin Pelletier

Jessie Huang

Carmen Lia Murall

Ahmad Pesaranghader

Vanda Gaonac'h-Lovejoy

David J. Hamelin

Raphael Poujol

Jean-Christophe Grenier

Martin W. Smith

Étienne Caron

Morgan Craig

Smita Krishnaswamy

B. Jesse Shapiro

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

2022-02-21

Frontiers in Medicine (published)

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages

Fatima Mostefai

Isabel Gamache

Arnaud N’Guessan

Justin Pelletier

Jessie Huang

Carmen Lia Murall

Ahmad Pesaranghader

Vanda Gaonac'h-Lovejoy

David J. Hamelin

Raphael Poujol

Jean-Christophe Grenier

Martin Smith

Etienne Caron

Morgan Craig

Smita Krishnaswamy

B. Jesse Shapiro

2022-02-21

Frontiers in Medicine (publié)

Genomic epidemiology and associated clinical outcomes of a SARS-CoV-2 outbreak in a general adult hospital in Quebec

Bastien Paré

Marieke Rozendaal

Sacha Morin

Raphael Poujol

Fatima Mostefai

Shawn M. Simpson

Jean-Christophe Grenier

Léa Kaufmann

Henry Xing

Miguelle Sanchez

Ariane Yechouron

Ronald Racette

Ivan Pavlov

Martin Smith

2021-12-02

PLoS ONE (publié)

Patient health records and whole viral genomes from an early SARS-CoV-2 outbreak in a Quebec hospital reveal features associated with favorable outcomes

Bastien Paré

Marieke Rozendaal

Sacha Morin

Léa Kaufmann

Shawn M. Simpson

Raphael Poujol

Fatima Mostefai

Jean-Christophe Grenier

Henry Xing

Miguelle Sanchez

Ariane Yechouron

Ronald Racette

Ivan Pavlov

Martin Smith

2021-12-02

PLoS ONE (publié)

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

Fatima Mostefai

Isabel Gamache

Jessie Huang

Arnaud N’Guessan

Justin Pelletier

Ahmad Pesaranghader

David J. Hamelin

Carmen Lia Murall

Raphael Poujol

Jean-Christophe Grenier

Martin Smith

Etienne Caron

Morgan Craig

Jesse Shapiro

Smita Krishnaswamy

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

2021-09-29

bioRxiv (prépublication)

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

Manik Kuchroo

Jessie Huang

Patrick Wong

Jean-Christophe Grenier

Dennis Shung

Alexander Tong

Carolina Lucas

Jon Klein

Daniel B. Burkhardt

Scott Gigante

Abhinav Godavarthi

Benjamin Israelow

Tianyang Mao

Ji Eun Oh

Julio Silva

Takehiro Takahashi

Camila D. Odio

Arnau Casanovas-Massana

John Fournier

Shelli Farhadian … (voir 7 de plus)

Charles S. Dela Cruz

Albert I. Ko

F. Perry Wilson

Akiko Iwasaki

Smita Krishnaswamy

2020-11-17

bioRxiv (prépublication)

Diet Networks: Thin Parameters for Fat Genomics

Adriana Romero Soriano

pierre luc carrier

Akram Erraqabi

Tristan Sylvain

Alex Auvolat

Etienne Dejoie

Marc-André Legault

Marie-Pierre Dubé

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

2017-01-01

ICLR.cc/2017/conference (poster)

Diet Networks: Thin Parameters for Fat Genomics

Adriana Romero Soriano

pierre luc carrier

Akram Erraqabi

Tristan Sylvain

Alex Auvolat

Etienne Dejoie

Marc-André Legault

Marie-Pierre Dubé

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

2017-01-01

ICLR.cc/2017/conference (poster)

Diet Networks: Thin Parameters for Fat Genomic

Adriana Romero Soriano

pierre luc carrier

Akram Erraqabi

Tristan Sylvain

Alex Auvolat

Etienne Dejoie

Marc-andr'e Legault

M. Dubé

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer: each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

2016-11-04

ArXiv (prépublication)