Portrait of Julie Hussin

Julie Hussin

Associate Academic Member
Associate Professor, Université de Montréal
Research Topics
Computational Biology
Data Mining
Deep Learning
Medical Machine Learning
Multimodal Learning
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Biography

Julie Hussin is an associate professor at the Department of Medecine at the Université de Montréal (UdeM) and a researcher at the Montreal Heart Institute (MHI). She holds a Canadian Research Chair (Tier 2) in Responsible Multi-Omics Data Science, and is the Chair of the graduate programs in bioinformatics at UdeM.

Dr. Hussin was trained in statistical and evolutionary genomics and has significant experience in handling multi-omics datasets from large population cohorts. Her work in computational biology is relevant to medical and population genomics, fields in which she contributed several methodological advances. Her interdisciplinary work aims to develop innovative tools for precision medicine. Her research projects focused on improving risk prediction and management of cardiometabolic disease, particularly in heart failure.

Her approaches integrate various data types, such as clinical, genetic, transcriptomic, proteomic and metabolomic data, to uncover new insights into the biological determinants of heart disease, notably through unsupervised learning techniques. In the context of the COVID-19 pandemic, her group also led the development of data science algorithms to analyze viral genetic data, aid viral surveillance efforts and study host-pathogen interactions and viral evolution. Her work also focuses on interpretability, generalizability, and fairness of machine learning algorithms in health research.

Dr. Hussin is dedicated to promoting fair, safe, and transparent AI in health research and strives for inclusivity and representation to ensure her work benefits all segments of the population. expertise also extends to the field of fair, safe and transparent AI for health research. She also teaches several undergraduate and graduate courses in computational biology and population genetics, as well as machine learning for genomics. Prior to joining UdeM as a professor, she was a Human Frontier Postdoctoral Fellow at the Wellcome Trust Centre for Human Genetics at the University of Oxford (Linacre College), and a Visiting Fellow at McGill University.

Current Students

Cantin Baron
Collaborating researcher - Université de Montréal
Website
Google Scholar
David Hamelin
PhD - Université de Montréal
Roberto Holgado
Postdoctorate - Université de Montréal
Website
Github
Google Scholar
Alexis Nolin-Lapalme
PhD - Université de Montréal
Camille Rochefort-Boulanger
PhD - Université de Montréal
Co-supervisor :
Yoshua Bengio
Github
Issam Saadie
Master's Research - Université de Montréal
Matthew Scicluna
PhD - Université de Montréal
Co-supervisor :
Guy Wolf
Github
Google Scholar
Yonglin Zhu
PhD - Université de Montréal

Publications

Chromatin landscape and enhancer-gene interaction differences between three cardiac cell types
Yan Zhu
Jean‐Christophe Grenier
Raphaël Poujol
Olivier Tastet
Caroline Lee
Svenja Koslowski
Marouane Benzaki
Talal Fawaz
Julie Hussin
Roger Foo
Chukwuemeka George Anene-Nzelu
Matthew Ackers-Johnson
2026-02-28
Journal of Molecular and Cellular Cardiology Plus (published)
doi.org
Foundation models for electrocardiogram interpretation: clinical implications
Alexis Nolin-Lapalme
Achille Sowa
Jacques Delfrate
Olivier Tastet
Denis Corbin
Merve Kulbay
Derman Ozdemir
Marie-Jeanne Noël
François-Christophe Marois-Blanchet
François Harvey
Surbhi Sharma
Minhaj Ansari
I-Min Chiu
Valentina D'souza
Sam F. Friedman
Michael Chassé
Brian J. Potter
Jonathan Afilalo
Pierre Adil Elias
Gilbert Jabbour … (see 13 more)
Mourad Bahani
Marie-Pierre Dubé
Patrick M. Boyle
Neal A. Chatterjee
Joshua Barrios
Geoffrey H. Tison
David Ouyang
Mahnaz Maddah
Shaan Khurshid
Julia Cadrin-Tourigny
Rafik Tadros
Julie Hussin
Robert Avram
The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for au… (see more)tomated interpretation often lack generalizability, remain closed source, and are primarily trained using supervised learning (SL), which requires extensive labelled datasets and may limit adaptability across diverse clinical settings. Self-supervised learning (SSL) can potentially overcome these limitations by learning robust representations from unlabelled data. To address these challenges, this study developed and compared two open-source foundational ECG models: DeepECG-SL, a supervised multilabel ECG model, and DeepECG-SSL, a self-supervised model. Both models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL leveraged unlabelled data through self-supervised contrastive learning and masked lead modelling before fine-tuning for downstream tasks, while DeepECG-SL was trained directly on labelled diagnostic data in an end-to-end fashion. Performance was evaluated across seven private, multilingual healthcare systems and four public ECG repositories, with assessment of fairness by age and sex, and investigation of privacy vulnerabilities as well as memory and compute requirements. DeepECG-SSL achieved micro-averaged area under the receiver operating characteristic curves (AUROCs) across all 77 cardiac conditions for ECG interpretation of 0.990 [95% confidence interval (CI): 0.990, 0.990] on the internal dataset (MHI-ds), 0.981 (95% CI: 0.981, 0.981) on external public datasets (UKB, CLSA, MIMIC-IV and PTB), and 0.983 (95% CI: 0.983, 0.983) on external private datasets (UW, UCSF, JGH, NYP, MGH, CSH and CHUM), while DeepECG-SL demonstrated AUROCs of 0.992 (95% CI: 0.992, 0.992), 0.980 (95% CI: 0.980, 0.980), and 0.983 (95% CI: 0.983, 0.984), respectively. Fairness analyses revealed minimal disparities (true-positive rate and false-positive rate difference <0.1) across age and sex groups for both models. DeepECG-SSL demonstrated superior performance on limited-data digital biomarker tasks, with the largest improvements in long QT syndrome (LQTS) genotype classification (AUROC 0.931 vs 0.850, P = .026, n = 127 ECGs) and 5 year atrial fibrillation risk prediction (AUROC 0.742 vs 0.734, P < 0.001, n = 132 050 ECGs), while achieving superior performance in left ventricular ejection fraction ≤40% classification (AUROC 0.926 vs 0.917, P < 0.001, n = 25 252 ECGs) and comparable performance in LQTS detection (AUROC 0.767 vs 0.735, P = 0.117, n = 934 ECGs). This study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics. By releasing model weights, preprocessing tools, and validation code, this work aims to support robust, data-efficient AI diagnostics across diverse clinical environments and questions.
2025-12-26
European Heart Journal (published)
doi.org
LIPTER, a cardiomyocyte-enriched long noncoding RNA, controls cardiac cytoskeletal maturation and is regulated by a cardiomyocyte-specific enhancer.
George Anene Nzelu
Mick Lee
Svenja Koslowski
Wenhao Zheng
Marouane Benzaki
Michelle Mak
Xiaohua Wang
Lek Wen Tan
Albert Dashi
Yonglin Zhu
Talal Fawaz
Kenneth Ng
Davy Pham
Francis LeBlanc
Guillaume Lettre
Julie Hussin
Roger Foo
Cardiac development is characterized by a complex series of molecular, cytoskeletal and electrophysiological changes that guarantee the prop… (see more)er functioning of adult cardiomyocytes (CMs). These changes are defined by cell-type-specific transcriptional rewiring of progenitor cells to form CMs, and are regulated by various epigenetic elements, such as long noncoding RNAs (lncRNAs). LncRNAs are versatile epigenetic regulators as they may act in cis or in trans to orchestrate important gene programs during cardiac development and may concurrently encode micropeptides. LIPTER is one such lncRNA, previously shown to regulate lipid droplet transport in cardiomyocytes and thus an important regulator of cardiomyocyte metabolism. Here we show that LIPTER also plays a role in the cytoskeletal maturation of CMs, as loss of LIPTER leads to persistent expression of fetal genes, changes in chromatin accessibility, disorganized sarcomeres and impaired calcium homeostasis in CMs. Furthermore, we have identified a cardiomyocyte-specific regulatory enhancer that regulates the expression of LIPTER in CMs. CRISPR-mediated inhibition of this enhancer led to reduced LIPTER expression in CMs and increased expression of fetal genes. This CM-specific enhancer could therefore be manipulated to control the expression of LIPTER for therapeutic benefit. In summary, we have unravelled a novel role of LIPTER in CMs cytoskeletal maturation and have identified a CM-specific enhancer for LIPTER.
2025-08-11
bioRxiv (preprint)
doi.org
LIPTER, a cardiomyocyte-enriched long noncoding RNA, controls cardiac cytoskeletal maturation and is regulated by a cardiomyocyte-specific enhancer
Chukwuemeka George Anene-Nzelu
C.J Mick Lee
Svenja Koslowski
Wenhao Zheng
Marouane Benzaki
Michelle Mak
Xiaohua Wang
Lek Wen Tan
Albert Dashi
Yonglin Zhu
Talal Fawaz
Kenneth Ng
Davy Pham
Francis LeBlanc
Guillaume Lettre
Julie Hussin
Roger Foo
2025-08-11
BioRxiv (preprint)
doi.org
Foundation models for generalizable electrocardiogram interpretation: comparison of supervised and self-supervised electrocardiogram foundation models
Alexis Nolin-Lapalme
Achille Sowa
Jacques Delfrate
Olivier Tastet
Denis Corbin
Merve Kulbay
Derman Ozdemir
Marie-Jeanne Noël
François-Christophe Marois-Blanchet
François Harvey
Surbhi Sharma
Minhaj Ansari
I-Min Chiu
Valentina Dsouza
Sam F. Friedman
Michael Chassé
Brian J. Potter
Jonathan Afilalo
Pierre Adil Elias
Gilbert Jabbour … (see 13 more)
Mourad Bahani
Marie-Pierre Dubé
Patrick M. Boyle
Neal A. Chatterjee
Joshua Barrios
Geoffrey H. Tison
David Ouyang
Mahnaz Maddah
Shaan Khurshid
Julia Cadrin-Tourigny
Rafik Tadros
Julie Hussin
Robert Avram
The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for au… (see more)tomated interpretation often lack generalizability, remain closed-source, and are primarily trained using supervised learning, limiting their adaptability across diverse clinical settings. To address these challenges, we developed and compared two open-source foundational ECG models: DeepECG-SSL, a self-supervised learning model, and DeepECG-SL, a supervised learning model. Both models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL was pretrained using self-supervised contrastive learning and masked lead modeling. The models were evaluated on six multilingual private healthcare systems and four public datasets for ECG interpretation across 77 diagnostic categories. Fairness analyses assessed disparities in performance across age and sex groups, while also investigating fairness and resource utilization. DeepECG-SSL achieved AUROCs of 0.990 (95%CI 0.990, 0.990) on internal dataset, 0.981 (95%CI 0.981, 0.981) on external public datasets, and 0.983 (95%CI 0.983, 0.983) on external private datasets, while DeepECG-SL demonstrated AUROCs of 0.992 (95%CI 0.992, 0.992), 0.980 (95%CI 0.980, 0.980) and 0.983 (95%CI 0.983, 0.983) respectively. Fairness analyses revealed minimal disparities (true positive rate & false positive rate difference<0.010) across age and sex groups. Digital biomarker prediction (Long QT syndrome (LQTS) classification, 5-year atrial fibrillation prediction and left ventricular ejection fraction (LVEF) classification) with limited labeled data, DeepECG-SSL outperformed DeepECG-SL in predicting 5-year atrial fibrillation risk (N=132,050; AUROC 0.742 vs. 0.720; Δ=0.022; P<0.001), identifying reduced LVEF ≤40% (N=25,252; 0.928 vs. 0.900; Δ=0.028; P<0.001), and classifying LQTS syndrome subtypes (N=127; 0.931 vs. 0.853; Δ=0.078; P=0.026). By releasing model weights, preprocessing tools, and validation code, we aim to support robust, data-efficient AI diagnostics across diverse clinical environments. This study establishes self-supervised learning as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics. Can self-supervised (SSL) learning yield ECG-based AI foundational models with enhanced performance, fairness, privacy, and generalizability compared to traditional supervised learning (SL) approaches? Our evaluation of DeepECG-SL and DeepECG-SSL across seven external health center datasets and four international publicly accessible datasets demonstrated that while both models achieve comparable diagnostic accuracy for ECG interpretation, SSL outperforms SL on novel tasks with smaller datasets. We validated DeepECG-SL and DeepECG-SSL across public and private datasets and demonstrated that SSL model had a superior generalizability by addressing fairness, privacy, and efficiency, and open sourcing our models, we advance ethical, adaptable AI for equitable, real-world ECG diagnostics. Graphical abstract: DeepECG-SL and DeepECG-SSL, two open-source AI models for 12-lead ECG interpretation, were trained on over 1 million ECGs. DeepECG-SSL, utilizing self-supervised contrastive learning and masked lead modeling, outperformed DeepECG-SL in utilizing digital biomarkers to predict atrial fibrillation risk, reduced LVEF, and long QT syndrome subtypes, while both models achieved high diagnostic accuracy with minimal fairness disparities across age and sex. Validated on ten external datasets, our work provides a robust, reproducible framework for equitable, efficient ECG-based cardiac diagnostics.
2025-03-04
medRxiv (published)
doi.org
Refining SARS-CoV-2 intra-host variation by leveraging large-scale sequencing data
Fatima Mostefai
Jean-Christophe Grenier
Raphaël Poujol
Julie Hussin
Understanding viral genome evolution during host infection is crucial for grasping viral diversity and evolution. Analyzing intra-host singl… (see more)e nucleotide variants (iSNVs) offers insights into new lineage emergence, which is important for predicting and mitigating future viral threats. Despite next-generation sequencing’s potential, challenges persist, notably sequencing artifacts leading to false iSNVs. We developed a workflow to enhance iSNV detection in large NGS libraries, using over 130 000 SARS-CoV-2 libraries to distinguish mutations from errors. Our approach integrates bioinformatics protocols, stringent quality control, and dimensionality reduction to tackle batch effects and improve mutation detection reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how related groups of data points are represented within a two-dimensional space, enhancing clustering structure explanation based on genetic similarities. This workflow advances accurate intra-host mutation detection, facilitating a deeper understanding of viral diversity and evolution.
2024-09-27
NAR Genomics and Bioinformatics (published)
doi.org
Temperature-dependent Spike-ACE2 interaction of Omicron subvariants is associated with viral transmission
Mehdi Benlarbi
Shilei Ding
Étienne Bélanger
Alexandra Tauzin
Raphaël Poujol
Halima Medjahed
Omar El Ferri
Yuxia Bo
Catherine Bourassa
Julie Hussin
Judith Fafard
Marzena Pazgier
Inès Levade
Cameron Abrams
Marceline Côté
Andrés Finzi
The continued evolution of severe acute respiratory syndrome 2 (SARS-CoV-2) requires persistent monitoring of its subvariants. Omicron subva… (see more)riants are responsible for the vast majority of SARS-CoV-2 infections worldwide, with XBB and BA.2.86 sublineages representing more than 90% of circulating strains as of January 2024. To better understand parameters involved in viral transmission, we characterized the functional properties of Spike glycoproteins from BA.2.75, CH.1.1, DV.7.1, BA.4/5, BQ.1.1, XBB, XBB.1, XBB.1.16, XBB.1.5, FD.1.1, EG.5.1, HK.3, BA.2.86 and JN.1. We tested their capacity to evade plasma-mediated recognition and neutralization, binding to angiotensin-converting enzyme 2 (ACE2), their susceptibility to cold inactivation, Spike processing, as well as the impact of temperature on Spike-ACE2 interaction. We found that compared to the early wild-type (D614G) strain, most Omicron subvariants' Spike glycoproteins evolved to escape recognition and neutralization by plasma from individuals who received a fifth dose of bivalent (BA.1 or BA.4/5) mRNA vaccine and improve ACE2 binding, particularly at low temperatures. Moreover, BA.2.86 had the best affinity for ACE2 at all temperatures tested. We found that Omicron subvariants’ Spike processing is associated with their susceptibility to cold inactivation. Intriguingly, we found that Spike-ACE2 binding at low temperature was significantly associated with growth rates of Omicron subvariants in humans. Overall, we report that Spikes from newly emerged Omicron subvariants are relatively more stable and resistant to plasma-mediated neutralization, present improved affinity for ACE2 which is associated, particularly at low temperatures, with their growth rates. The persistent evolution of SARS-CoV-2 gave rise to a wide range of variants harboring new mutations in their Spike glycoproteins. Several factors have been associated with viral transmission and fitness such as plasma-neutralization escape and ACE2 interaction. To better understand whether additional factors could be of importance in SARS-CoV-2 variants’ transmission, we characterize the functional properties of Spike glycoproteins from several Omicron subvariants. We found that the Spike glycoprotein of Omicron subvariants presents an improved escape from plasma-mediated recognition and neutralization, Spike processing, and ACE2 binding which was further improved at low temperature. Intriguingly, Spike-ACE2 interaction at low temperature is strongly associated with viral growth rate, as such, low temperatures could represent another parameter affecting viral transmission.
2024-07-01
mBio (published)
doi.org
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 … (see 7 more)
Charles S. Dela Cruz
Albert I. Ko
F. Perry Wilson
Julie Hussin
Guy Wolf
Akiko Iwasaki
Smita Krishnaswamy
Abstract

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of… (see more) patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16-hi,CD66b-lo neutrophil and IFNγ+,GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

2020-11-16
bioRxiv (preprint)
doi.org