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
Github

Publications

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics
Qingtian Zhu
Yumin Zheng
Yuling Sang
Yifan Zhan
Ziyan Zhu
Jun Ding
Yinqiang Zheng
Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distr… (see more)ibution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability of Implicit Neural Representations (INRs) that can enhance both the spatial density and the gene expression. Concretely within the proposed SUICA, we incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots and provide informative embeddings that are structure-aware for spatial mapping. We also tackle the extremely skewed distribution in a regression-by-classification fashion and enforce classification-based loss functions for the optimization of SUICA. By extensive experiments of a wide range of common ST platforms under varying degradations, SUICA outperforms both conventional INR variants and SOTA methods regarding numerical fidelity, statistical correlation, and bio-conservation. The prediction by SUICA also showcases amplified gene signatures that enriches the bio-conservation of the raw data and benefits subsequent analysis. The code is available at https://github.com/Szym29/SUICA.
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
arxiv.org
SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics
Qingtian Zhu
Yumin Zheng
Yuling Sang
Yifan Zhan
Ziyan Zhu
Jun Ding
Yinqiang Zheng
Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distr… (see more)ibution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability of Implicit Neural Representations (INRs) that can enhance both the spatial density and the gene expression. Concretely within the proposed SUICA, we incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots and provide informative embeddings that are structure-aware for spatial mapping. We also tackle the extremely skewed distribution in a regression-by-classification fashion and enforce classification-based loss functions for the optimization of SUICA. By extensive experiments of a wide range of common ST platforms under varying degradations, SUICA outperforms both conventional INR variants and SOTA methods regarding numerical fidelity, statistical correlation, and bio-conservation. The prediction by SUICA also showcases amplified gene signatures that enriches the bio-conservation of the raw data and benefits subsequent analysis.
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (published)
proceedings.mlr.press
Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models
Darcy Elizabeth Wagner
Hani N. Alsafadi
Nilay Mitash
Aurelien Justet
Qianjiang Hu
Ricardo Pineda
Claudia Staab-Weijnitz
Martina Korfei
Nika Gvazava
Kristin Wannemo
Ugochi Onwuka
Molly Mozurak
Adriana Estrada-Bernal
Juan Cala Garcia
Katrin Mutze
Rita Costa
Deniz Bölükbas
John Stegmayr
Wioletta Skronska-Wasek
Stephan Klee … (see 14 more)
Chiharu Ota
Hoeke A. Baarsma
Jingtao Wang
John Sembrat
Anne Hilgendorff
Jun Ding
Andreas Günther
Rachel Chambers
Ivan O Rosas
Stijn de Langhe
Naftali Kaminski
Mareike Lehmann
Oliver Eickelberg
Melanie Königshoff
Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (see more)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.
2025-08-02
Nature Communications (published)
doi.org
Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models
Darcy Elizabeth Wagner
Hani N. Alsafadi
Nilay Mitash
Aurelien Justet
Qianjiang Hu
Ricardo Pineda
Claudia Staab-Weijnitz
Martina Korfei
Nika Gvazava
Kristin Wannemo
Ugochi Onwuka
Molly Mozurak
Adriana Estrada-Bernal
Juan Cala Garcia
Katrin Mutze
Rita Costa
Deniz Bölükbas
John Stegmayr
Wioletta Skronska-Wasek
Stephan Klee … (see 14 more)
Chiharu Ota
Hoeke A. Baarsma
Jingtao Wang
John Sembrat
Anne Hilgendorff
Jun Ding
Andreas Günther
Rachel Chambers
Ivan O Rosas
Stijn de Langhe
Naftali Kaminski
Mareike Lehmann
Oliver Eickelberg
Melanie Königshoff
Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (see more)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.
2025-08-02
Nature Communications (published)
doi.org
Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis in preclinical models
Darcy Elizabeth Wagner
Hani N. Alsafadi
Nilay Mitash
Aurelien Justet
Qianjiang Hu
Ricardo Pineda
Claudia Staab-Weijnitz
Martina Korfei
Nika Gvazava
Kristin Wannemo
Ugochi Onwuka
Molly Mozurak
Adriana Estrada-Bernal
Juan Cala Garcia
Katrin Mutze
Rita Costa
Deniz Bölükbas
John Stegmayr
Wioletta Skronska-Wasek
Stephan Klee … (see 14 more)
Chiharu Ota
Hoeke A. Baarsma
Jingtao Wang
John Sembrat
Anne Hilgendorff
Jun Ding
Andreas Günther
Rachel Chambers
Ivan O Rosas
Stijn de Langhe
Naftali Kaminski
Mareike Lehmann
Oliver Eickelberg
Melanie Königshoff
Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix deposition. Current … (see more)IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive extracellular matrix production in IPF. Here we show that fibrotic alveolar type II cells regulate production and crosslinking of extracellular matrix via the co-transcriptional activator YAP. YAP leads to increased expression of Lysl oxidase (LOX) and subsequent LOX-mediated crosslinking by fibrotic alveolar type II cells. Pharmacological YAP inhibition via verteporfin reverses fibrotic alveolar type II cell reprogramming and LOX expression in experimental lung fibrosis in vivo and in human fibrotic tissue ex vivo. We thus identify YAP-TEAD/LOX inhibition in alveolar type II cells as a promising potential therapy for IPF patients.
2025-08-02
Nature Communications (published)
doi.org
Computational Tracking of Cell Origins Using CellSexID from Single-Cell Transcriptomes
Huilin Tai
Qian Li
Jingtao Wang
Jiahui Tan
Bowen Zhao
Ryann Lang
Basil J. Petrof
Jun Ding
Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplanta… (see more)tion research. Existing methods such as fluorescent labeling and genetic barcoding are technically demanding, costly, and often impractical for dynamic or heterogeneous tissues. Here, we introduce CellSexID, a computational framework that leverages sex as a surrogate marker for cell origin inference. Using a machine learning model trained on single-cell transcriptomic data, CellSexID accurately predicts the sex of individual cells, enabling in silico distinction between donor and recipient cells in sex-mismatched settings. The model identifies minimal sex-linked gene sets through ensemble feature selection and has been validated using both public datasets and experimental flow sorting, confirming the biological relevance of predicted populations. We further demonstrate CellSexID’s applicability beyond chimeric models, including organ transplantation and multiplexed sample demultiplexing. As a scalable and cost-effective alternative to physical labeling, CellSexID facilitates precise cell tracking and supports diverse biomedical applications involving mixed cellular origins.
2025-07-30
bioRxiv (preprint)
doi.org
DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads
Kailu Song
Yumin Zheng
Bowen Zhao
David H. Eidelman
Jian Tang
Jun Ding
2025-07-04
Nature Communications (published)
doi.org
Harnessing agent-based frameworks in CellAgentChat to unravel cell-cell interactions from single-cell and spatial transcriptomics
Vishvak Raghavan
Yumin Zheng
Yue Li
Jun Ding
2025-07-01
Genome Research (published)
doi.org
Alveolar epithelial cell plasticity and injury memory in human pulmonary fibrosis
Taylor S Adams
Jonas C Schupp
Agshin Balayev
Johad Khoury
Aurelien Justet
Fadi Nikola
Laurens J De Sadeleer
De Sadeleer J Laurens
Juan Cala Garcia
Marta Zapata-Ortega
Panayiotis V Benos
Benos V Panayiotis
P.V. Benos
John E McDonough
Farida Ahangari
Melanie Königshoff
Jun Ding
Robert J Homer
Ivan O Rosas
Xiting Yan … (see 3 more)
Bart M Vanaudenaerde
Wim A Wuyts
Naftali Kaminski
2025-06-21
bioRxiv (preprint)
doi.org
A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.
Yumin Zheng
Jonas C Schupp
Taylor S Adams
Geremy Clair
Aurelien Justet
Farida Ahangari
Xiting Yan
Paul Hansen
Marianne Carlon
Emanuela Cortesi
Marie Vermant
Robin Vos
De Sadeleer J Laurens
Ivan O Rosas
Ricardo Pineda
John Sembrat
Melanie Königshoff
John E McDonough
Bart M. Vanaudenaerde
Wim A Wuyts … (see 2 more)
Naftali Kaminski
Jun Ding
2025-06-20
Nature Biomedical Engineering (published)
doi.org
Advancing global antifungal development to combat invasive fungal infection
Xiu-Li Wang
Jun Ding
Koon Ho Wong
Chen Ding
Chang-Bin Chen
Wen-Juan Wu
Ningning Liu
2025-06-01
hLife (published)
doi.org
DTractor enhances cell type deconvolution in spatial transcriptomics by integrating deep neural networks, transfer learning, and matrix factorization
Yong Jin Kweon
Chenyu Liu
Gregory Fonseca
Jun Ding
2025-04-18
bioRxiv (preprint)
doi.org