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

An enhanced wideband tracking method for characteristic modes

Chao Huang

Chenjiang Guo

Xia Ma

Yi Yuan

Jun Ding

An enhanced wideband tracking method for characteristic modes (CMs) is investigated in this paper. The method consists of three stages, and … (see more)its core tracking stage (CTS) is based on a classical eigenvector correlation-based algorithm. To decrease the tracking time and eliminate the crossing avoidance (CRA), we append a commonly used eigenvalue filter (EF) as the preprocessing stage and a novel postprocessing stage to the CTS. The proposed postprocessing stage can identify all CRA mode pairs by analyzing their trajectory and correlation characteristics. Subsequently, it can predict corresponding CRA frequencies and correct problematic qualities rapidly. Considering potential variations in eigenvector numbers at consecutive frequency samples caused by the EF, a new execution condition for the adaptive frequency adjustment in the CTS is introduced. Finally, CMs of a conductor plate and a fractal structure are investigated to demonstrate the performance of the proposed method, and the obtained results are discussed.

2024-02-12

International Journal of Microwave and Wireless Technologies (published)

doi.org

Author Correction: BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.

Kim A. Tran

Erwan Pernet

Mina Sadeghi

Jeffrey Downey

Julia Chronopoulos

Elizabeth Lapshina

Oscar Tsai

Eva Kaufmann

Jun Ding

Maziar Divangahi

2024-02-01

Nature Immunology (published)

doi.org

BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.

Kim A. Tran

Erwan Pernet

Mina Sadeghi

Jeffrey Downey

Julia Chronopoulos

Elizabeth Lapshina

Oscar Tsai

Eva Kaufmann

Jun Ding

Maziar Divangahi

2024-01-15

Nature Immunology (published)

doi.org

BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.

Kim A. Tran

Erwan Pernet

Mina Sadeghi

Jeffrey Downey

Julia Chronopoulos

Elizabeth Lapshina

Oscar Tsai

Eva Kaufmann

Jun Ding

Maziar Divangahi

2024-01-15

Nature Immunology (published)

doi.org

BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.

Kim A. Tran

Erwan Pernet

Mina Sadeghi

Jeffrey Downey

Julia Chronopoulos

Elizabeth Lapshina

Oscar Tsai

Eva Kaufmann

Jun Ding

Maziar Divangahi

2024-01-15

Nature Immunology (published)

doi.org

BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.

Kim A. Tran

Erwan Pernet

Mina Sadeghi

Jeffrey Downey

Julia Chronopoulos

Elizabeth Lapshina

Oscar Tsai

Eva Kaufmann

Jun Ding

Maziar Divangahi

2024-01-15

Nature Immunology (published)

doi.org

Unagi: 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

Paul Hansen

Xiting Yan

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

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap rem… (see more)ains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI’s cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI’s predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

2023-12-18

Research Square (published)

doi.org

scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Xiuhui Yang

Koren K. Mann

Hao Wu

Jun Ding

Single-cell multi-omics illuminate intricate cellular states, yielding transformative insights into cellular dynamics and disease. Yet, whil… (see more)e the potential of this technology is vast, the integration of its multifaceted data presents challenges. Some modalities have not reached the robustness or clarity of established scRNA-seq. Coupled with data scarcity for newer modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross: a tool adeptly engineered using variational autoencoder, generative adversarial network principles, and the Mutual Nearest Neighbors (MNN) technique for modality alignment. This synergy ensures seamless integration of varied single-cell multi-omics data. Beyond its foundational prowess in multi-omics data integration, scCross excels in single-cell cross-modal data generation, multi-omics data simulation, and profound in-silico cellular perturbations. Armed with these capabilities, scCross is set to transform the field of single-cell research, establishing itself in the nuanced integration, generation, and simulation of complex multi-omics data.

2023-12-15

bioRxiv (preprint)

doi.org

scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data

Yiming Jia

Hao Wu

Jun Ding

2023-11-27

bioRxiv (preprint)

doi.org

scSniper: Single-cell Deep Neural Network-based Identification of Prominent Biomarkers

Mingyang Li