Portrait of Jun Ding

Jun Ding

Affiliate Member
Assistant professor, McGill University, Department of Medicine
Research Topics
Computational Biology
Medical Machine Learning
Representation Learning
Website
Google Scholar

Biography

Jun Ding is an assistant professor in the Department of Medicine of the Faculty of Medicine and Health Sciences at McGill University.

Alongside his team, he is dedicated to employing machine learning techniques to decipher the complex dynamics of cells in various diseases, such as developmental disorders, pulmonary diseases and cancers. The diverse and intricate nature of these conditions necessitates innovative approaches, prompting the use of state-of-the-art single-cell technologies to meticulously profile individual cell states. The result is a rich source of data for our machine learning models.

These technologies present unprecedented opportunities to advance understanding, particularly in fields like developmental and cancer biology. However, the challenge is to develop computational models capable of linking this intricate biomedical data to potential discoveries.

Ding’s primary focus lies in the development and refinement of machine learning methodologies, especially probabilistic graphical models, to effectively analyze, model and visualize both single-cell and bulk omics data, often featuring longitudinal or spatial dimensions. The goal is to harness these advanced machine learning techniques to deepen the comprehension of cellular dynamics, and so develop groundbreaking diagnostic and therapeutic strategies that can significantly benefit public health.

Current Students

Vishvak Raghavan
PhD - McGill University
Principal supervisor :
Yue Li
vishvak.raghavan@mila.quebec
Github

Publications

Cross-component causal coupling modeling and multi-order spectral representation for interpretable mechanical fault diagnosis
Yi Gao
Haidong Shao
Jun Ding
Jiarui Liu
Mingze Xu
Jiale Zhang
Daolin Xu
2026-10-31
Engineering Applications of Artificial Intelligence (published)
doi.org
ATAD2 is a novel regulator of myogenesis and autophagy in skeletal muscles
Sami Sedraoui
Alaa Moamer
Tomer Jordi Chaffer
Jean-Philippe Leduc-Gaudet
Dominique Mayaki
Hanfen Shi
Ryann Lang
Minna Woo
Yumin Zheng
Jun Ding
Marco Sandri
Gilles Gouspillou
Sabah N.A. Hussain
The nuclear protein ATAD2 (ATPase family AAA domain containing 2) is a known positive regulator of cell proliferation in various cancer type… (see more)s. The expression and functional roles of ATAD2 in skeletal muscle cells are unknown. In this study, we used transient and stable knockdown approaches using siRNA and shRNA oligos to evaluate how ATAD2 regulates proliferation, migration, differentiation, and autophagy in C2C12 myoblasts. Atad2 knockdown (KD) significantly increased myoblast proliferation rate, S-phase entry, overall cell viability, and early differentiation into myotubes. However, Atad2 KD also elicited myotube atrophy and upregulation of ubiquitin E3 ligases Atrogin-1 and MuRF1. Basal autophagy was inhibited in Atad2 KD cells as a result of downregulation of autophagy-related genes ( Lc3b , Gabarapl1 , Atg5 , and Atg7 ). Immunoblotting and immunostaining revealed significant decrease in LC3B protein levels and the number of LC3B punctae per cell with Atad2 KD, respectively. LAMP1 staining confirmed the presence of enlarged lysosomes in Atad2 KD cells. Additionally, genes involved in lysosome function and integrity including Rab7 , Rab29 , Rab32 , Nrbf2 , and Cathepsin L were downregulated in Atad2 KD cells. Collectively, these results indicate that ATAD2 is a critical regulator of muscle cell proliferation, differentiation, autophagy and lysosomal integrity.
2026-09-04
Scientific Reports (published)
doi.org
Robust inference and correlates from genetic associations with personality
Ted Schwaba
Margaret L. Clapp Sullivan
Wonuola A. Akingbuwa
Kerli Ilves
Peter T. Tanksley
Camille M. Williams
Yavor Dragostinov
Travis T. Mallard
Justin D. Tubbs
Wangjingyi Liao
Lindsay S. Ackerman
Josephine C. M. Fealy
Gibran Hemani
Javier de la Fuente
George Davey Smith
Priya Gupta
Murray B. Stein
Joel Gelernter
Daniel F. Levey
Urmo Võsa … (see 121 more)
Liisi Ausmees
Anu Realo
Tõnu Esko
Mariliis Vaht
Jüri Allik
Tõnu Esko
René Mõttus
Uku Vainik
Gudrun A. Jonsdottir
Gudmar Thorleifsson
Árni Freyr Gunnarsson
Gyda Bjornsdottir
Thorgeir E. Thorgeirsson
Hreinn Stefansson
Kari Stefansson
Rosa Cheesman
Qi Qin
Elizabeth C. Corfield
Helga Ask
Fartein Ask Torvik
Eivind Ystrom
Martin Tesli
Dorret I. Boomsma
Eco J. C. de Geus
Jouke-Jan Hottenga
Dener Cardoso Melo
Harold Snieder
Catharina A. Hartman
Charley Xia
Archie Campbell
Michelle Luciano
Ian J. Deary
W. David Hill
Seon-Kyeong Jang
Scott I. Vrieze
Gonçalo Abecasis
Michelle K. Lupton
Brittany L. Mitchell
Petra V. Viher
Lucía Colodro-Conde
Nicholas G. Martin
Sarah E. Medland
Eske M. Derks
Briar Wormington
Jaakko Kaprio
Karri Silventoinen
Teemu Palviainen
Agnieszka Musial
Kaili Rimfeld
Robert Plomin
Margherita Malanchini
Danielle M. Dick
Fazil Aliev
COGA Collaborators
The Spit for Science Working Group
Laura W. Wesseldijk
Fredrik Ullén
Miriam A. Mosing
Henry R. Kranzler
Yaira Nunez
Sarah Beck
Renato Polimanti
Tobias Edwards
Alexandros Giannelis
Emily A. Willoughby
James J. Lee
Matt McGue
Antonio Terracciano
Michele Marongiu
Edoardo Fiorillo
Francesco Cucca
Angelina R. Sutin
Peter J. van der Most
Albertine J. Oldehinkel
Tina Kretschmer
Andrey A. Shabalin
Anna R. Docherty
Robert F. Krueger
Colin D. Freilich
Binisha H. Mishra
Terho Lehtimäki
Olli T. Raitakari
Mika Kähönen
Aino Saarinen
Henrik Dobewall
Liisa Keltikangas-Järvinen
Klaus Berger
Marisol Herrera-Rivero
Fabian Streit
Swapnil Awasthi
Stephanie H. Witt
Johanna Tuhkanen
Katri Räikkönen
Johan G. Eriksson
Jari Lahti
Gail Davies
Paul Redmond
Adele Taylor
Janie Corley
Tom C. Russ
Marina Ciullo
Teresa Nutile
Jun Ding
Yong Qian
Toshiko Tanaka
Luigi Ferrucci
Lea Zillich
Lea Sirignano
K. Paige Harden
Erhan Genç
Patrick D. Gajewski
Stephan Getzmann
Christoph Fraenz
Javier E. Schneider Peñate
Stefanie Lis
Alisha S. M. Hall
Christian Schmahl
Sabine C. Herpertz
Abdel Abdellaoui
Michel G. Nivard
Elliot M. Tucker-Drob
Personality traits describe stable differences in how people think, feel and behave, and how they interact with and experience their social … (see more)and physical environments1,2. Many questions remain unanswered about associations between DNA and personality traits, such as their robustness, their generalizability and the biological and social pathways through which they act. Here we meta-analyse data across 46 cohorts comprising 611,037 to 1.14 million participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism and openness to experience), and data from up to 50,725 participants for within-family GWAS. We identify 1,260 lead genetic variants associated with personality, including 824 novel variants3. Common genetic variants explain a moderate 4.8-9.3% of the variance in measures of each trait, and 9.3-13.3% among instruments with typical measurement reliability. Genetic associations with personality are highly consistent but not identical across geography, reporter (self versus close other), age group and measurement instrument, and we find minimal spousal assortment for personality in recent history. In contrast to many other social and behavioural traits4,5, within-family GWAS and polygenic index analyses indicate that genetic associations with personality are minimally confounded by the shared family environment. Polygenic prediction, genetic correlation and Mendelian randomization analyses indicate that personality traits have widespread, potentially causal associations with consequential behaviours and life outcomes. Overall, we find that the genetic architecture of personality is robustly generalizable, minimally confounded and widely relevant to human experience.
2026-09-01
Nature (published)
doi.org
Preparation of MgAl2O4-reinforced magnesium composite refractories from high silicon magnesite tailings: α-Al2O3 /AlN reaction mechanism and thermal shock resistance
Sheng Wu
Qingdong Hou
Xudong Luo
Jun Ding
Cairan Wang
Jinfan Xu
2026-07-31
Journal of Alloys and Compounds (published)
doi.org
ProtSyntax: a protein large language model for decoding post-translational modification syntax and function
Yiyu Lin
Jiahui Wu
You Zhou
Xinye Ni
Shan Chang
Jun Ding
Yan Wang
Xin Gao
Sen Yang
Post-translational modifications (PTMs) expand protein function by encoding context-dependent regulatory states, and their dysregulation con… (see more)tributes to cancer, neurodegeneration and metabolic disease. However, existing methods treat PTMs as independent residue labels, limiting their ability to distinguish contextually permissible sites, model crosstalk and infer functional consequences. Here we introduce ProtSyntax, a PTM-aware foundation protein language model combining protein-aware positional encoding, bidirectional state-space propagation, geometry-constrained attention and adaptive multi-objective learning. This design integrates residue chemistry, motif order, long-range context and three-dimensional microenvironments while coupling PTM recognition to enzyme function. Across 40 PTM-site benchmarks, ProtSyntax exceeded the strongest baselines in mean MCC and AP by 12.66% and 10.67%. ProtSyntax also recovered masked PTM types and sites, rejected structural decoys, generalized to data-scarce modifications, reconstructed crosstalk and linked PTM perturbations to enzyme kinetics. Applications to pathogenic variants, biomolecular condensates and disease-associated PTM landscapes demonstrate its potential to decode the regulatory language of the modified proteome.
2026-07-27
bioRxiv (preprint)
doi.org
Fine-tuned large language models enhance influenza forecasting
Chenxiang Li
Wenjing Gao
Qiqiao Zhang
Yue Zhang
Bowen Zhao
Jule Yang
Li Qi
Jun Ding
Dechao Tian
Influenza-like illness (ILI) remains a persistent global health challenge, necessitating accurate forecasting tools for timely public health… (see more) response. This study systematically benchmarks fine-tuned large language models (LLMs), e.g., Llama2 and GPT2, for influenza surveillance forecasting in data-limited time-series settings. We develop a lightweight fine-tuning framework that adapts pre-trained LLMs using compact embedding and prediction layers and evaluate it on seven weekly aggregated real-world surveillance datasets. Despite sample sizes of only ∼523 time points per region and the absence of cloud-based data transfer, fine-tuned LLMs consistently outperform SARIMA, LSTM, PatchTST, CoVTransformer, FEDformer, Time-LLM, and GPT4TS in both accuracy and stability, especially for long-term forecasts across diverse geographic settings. Even in zero-shot settings, pre-trained LLMs capture broad epidemic trends with performance comparable to SARIMA. These findings establish fine-tuned LLMs as efficient and robust forecasting tools suitable for privacy-sensitive, data-scarce public health applications.
2026-06-30
Cell Reports Methods (published)
doi.org
High-efficiency wave-propulsion of a hydrofoil with bistable restoring stiffness
Yuhang Kang
Haicheng Zhang
Jiarui Liu
Yuheng Chen
Yiming Lu
Jun Ding
Daolin Xu
2026-06-30
Journal of Ocean Engineering and Science (published)
doi.org
Molecular pathways of immune checkpoint inhibitor–induced hepatitis.
Erika Bushatsky
Natasha Ryan
Manuel Flores Molina
Steph A. Pang
Judith Lapierre
Madelyn Abraham
Sonia del Rincón
Jun Ding
Marie Hudson
Wilson H. Miller
2573 Background: Immune checkpoint inhibitor (ICI) related hepatitis is a clinically significant immune-related adverse event (irAE) a… (see more)nd a common cause of treatment interruption. It occurs in roughly 5 to 10 percent of patients receiving anti PD-(L)1 monotherapy and in up to one third of those treated with combination ICI therapy. Despite increasing clinical recognition, the molecular mechanisms and predictive factors underlying ICI hepatitis remain poorly defined. The Montreal Immune-Related Adverse Events (MIRAE)-led hepatitis project aims to characterize the immune cell populations and underlying transcriptional programs associated with ICI-hepatitis pathogenesis. Methods: This translational study is conducted within the MIRAE biobank, a prospective multicenter cohort of ICI-treated patients with and without irAEs. The hepatitis cohort includes patients with longitudinal plasma samples collected at baseline, on treatment, and at irAE onset. Ongoing immune profiling efforts include plasma-based cytokine and chemokine analysis, high-throughput plasma proteomics, and single cell RNA sequencing of PBMCs. Preliminary analysis focused on plasma proteomics. Five patients with high-grade ICI-hepatitis and five ICI-treated controls without irAEs were selected and matched by age, sex, and primary tumor. Plasma samples were analyzed using the SomaScan 11K assay to identify differentially expressed proteins and enriched immune pathways. Results: ICI-related hepatitis was clinically severe, requiring systemic corticosteroids in all cases and additional immunosuppressive therapies in most patients. ICI-hepatitis cases showed significantly higher plasma levels of liver injury markers, including ALT and AST, compared with matched controls. Widespread alterations were observed in the circulating proteome, with strong upregulation of liver-enriched proteins and inflammatory mediators. Gene set enrichment analyses revealed enrichment of liver-associated pathways including xenobiotic and bile acid metabolism, as well as IL-12 signaling, interferon-α and γ, neutrophil-associated pathways, and liver-resident macrophage signatures. Pathway analysis of single cell data revealed enhanced cytotoxic activity of CD8 T cells during ICI hepatitis, as exemplified by upregulation of the CTL and IL-6 pathways. Conclusions: ICI-hepatitis was associated with circulating immune signature characterized by liver injury markers, inflammatory mediators, and enrichment of innate immune pathways. These findings provide molecular insight into the immunopathogenesis of ICI hepatitis and inform future biomarker discovery, druggable pathways, and risk stratification.
2026-05-31
Journal of Clinical Oncology (published)
doi.org
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Yu Li
Jun Ding
Yue Li
Single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) enables investigation of open chromatin landscapes at si… (see more)ngle-cell resolution, but its analysis remains challenging because of sparsity, noise, and dataset-specific peak vocabularies. Genome Foundation Models (GFMs), pre-trained on large DNA sequence corpora, offer a potential source of transferable sequence information for scATAC-seq modeling. We introduce the Genome Foundation Embedded Topic Model (\model{}), an interpretable framework that combines GFMs with the Embedded Topic Model (ETM) for sequence-informed scATAC-seq analysis. By integrating GFM-derived DNA sequence embeddings into a topic-model decoder, \model{} improves clustering quality on standard benchmarks and captures cell-state-specific transcription factor activity through motif scoring and attention-based interpretation.
2026-05-27
FM4LS @ International Conference on Machine Learning (poster)
openreview.net
openreview.net
Sex-specific hormone-sensitive regulatory architecture in adolescence as a scaffold for depression vulnerability
Gladi Thng
Michel Garcia-Miranda
Kailu Song
Anjali Chawla
Reine Khoury
Minh Nguyen
Gabriella Frosi
Matthew Suderman
David Liao
Natalina Salmaso
Tie Yuan Zhang
Pan Wong Tak
Yashar Zeighami
Jun Ding
Corina Nagy
2026-05-20
bioRxiv (preprint)
doi.org
RFGWRK: a hybrid downscaling framework for high-resolution precipitation mapping in geohazard-prone mountainous regions
Simin Zhang
Zeshuang Zheng
Jun Ding
Shengbing Yang
Yuan Zeng
2026-05-18
International Conference on Remote Sensing, Surveying, and Mapping (published)
doi.org
Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent
Vishvak Raghavan
Brent Yoon
Gregory J Fonseca
Yue Li
Jun Ding
RNA velocity enables inference of cell state transitions from single-cell transcriptomics by modeling transcriptional dynamics from spliced … (see more)and unspliced mRNA. However, existing methods overlook spatial context and struggle to scale to large datasets, limiting insights into tissue organization and dynamic processes. We introduce veloAgent, a deep generative and agent-based framework that estimates gene- and cell-specific transcriptional kinetics while integrating spatial information through agent-based simulations of local microenvironments. By leveraging both molecular and spatial cues, veloAgent improves velocity accuracy and achieves sublinear memory scaling, enabling efficient analysis of large and multi-batch spatial datasets. A distinctive feature of veloAgent is its in silico perturbation module, which allows targeted manipulation of spatial velocity vectors to simulate regulatory interventions and predict their impact on cell fate dynamics. These capabilities position veloAgent as a scalable and versatile framework for dissecting spatially resolved cellular dynamics and guiding cell fate manipulation across diverse biological processes.
2026-05-05
Molecular Systems Biology (published)
doi.org