Jun Ding

Membre affilié

Professeur adjoint, McGill University, Département de médecine

Biographie

Jun Ding est professeur adjoint au Département de médecine de la Faculté de médecine et des sciences de la santé de l'Université McGill. Aux côtés de son équipe, il se consacre à l'utilisation de techniques d'apprentissage automatique pour éclaircir les dynamiques complexes des cellules dans diverses maladies, telles que les troubles du développement, les maladies pulmonaires et les cancers. La nature diversifiée et complexe de ces affections nécessite l’usage d’approches innovantes, incitant à l'utilisation de technologies unicellulaires de pointe. Ces technologies offrent des possibilités sans précédent pour faire avancer la compréhension, notamment dans des domaines tels que la biologie du développement et du cancer. Cependant, elles posent également des défis dans le développement de modèles informatiques capables de relier ces données biomédicales complexes à des découvertes potentielles.

Jun Ding a comme objectif le développement et l'affinement des méthodologies d'apprentissage automatique, en particulier des modèles graphiques probabilistes, pour analyser, modéliser et visualiser efficacement des données omiques à la fois de cellules uniques et de cellules groupées, souvent avec des dimensions longitudinales ou spatiales. Le but de ses recherches est d'utiliser ces techniques avancées d'apprentissage automatique pour approfondir la compréhension des dynamiques cellulaires, afin de développer des stratégies diagnostiques et thérapeutiques novatrices susceptibles de bénéficier considérablement à la santé publique.

Étudiants actuels

Vishvak Raghavan

Maîtrise recherche - McGill University

vishvak.raghavan@mila.quebec

Github

Publications

GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling

Yimin Fan

Yu Li

Jun Ding

Yue Li

2024-05-17

Lecture Notes in Computer Science (publié)

doi.org

RAMEN Unveils Clinical Variable Networks for COVID-19 Severity and Long COVID Using Absorbing Random Walks and Genetic Algorithms

Yiwei Xiong

Jingtao Wang

Xiaoxiao Shang

Tingting Chen

Douglas D. Fraser

Gregory Fonseca

Simon Rousseau

Jun Ding

The COVID-19 pandemic has significantly altered global socioeconomic structures and individual lives. Understanding the disease mechanisms a… (voir plus)nd facilitating diagnosis requires comprehending the complex interplay among clinical factors like demographics, symptoms, comorbidities, treatments, lab results, complications, and other metrics, and their relation to outcomes such as disease severity and long term outcomes (e.g., post-COVID-19 condition/long COVID). Conventional correlational methods struggle with indirect and directional connections among these factors, while standard graphical methods like Bayesian networks are computationally demanding for extensive clinical variables. In response, we introduced RAMEN, a methodology that integrates Genetic Algorithms with random walks for efficient Bayesian network inference, designed to map the intricate relationships among clinical variables. Applying RAMEN to the Biobanque québécoise de la COVID-19 (BQC19) dataset, we identified critical markers for long COVID and varying disease severity. The Bayesian Network, corroborated by existing literature and supported through multi-omics analyses, highlights significant clinical variables linked to COVID-19 outcomes. RAMEN’s ability to accurately map these connections contributes substantially to developing early and effective diagnostics for severe COVID-19 and long COVID.

2024-02-27

bioRxiv (prépublication)

doi.org

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 … (voir plus)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 (publié)

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 (publié)

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 (publié)

doi.org

MATES: A Deep Learning-Based Model for Locus-specific Quantification of Transposable Elements in Single Cell

Ruohan Wang

Yumin Zheng

Zijian Zhang

Xiaopeng Zhu

Tao P. Wu

Jun Ding

Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align mu… (voir plus)lti-mapping reads to either ‘best-mapped’ or ‘random-mapped’ locations and categorize them at sub-family levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a novel deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved performance over existing methods, enhancing the accuracy of TE quantification and aiding in the identification of marker TEs for identified cell populations. This development enables exploring single-cell heterogeneity and gene regulation through the lens of TEs, offering a transformative tool for the single-cell genomics community.

2024-01-11

bioRxiv (prépublication)

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… (voir plus)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 (prépublication)

doi.org

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

Yingfu Wu

Zhenqi Shi

Xiangfei Zhou

Pengyu Zhang

Xiuhui Yang

Jun Ding

Hao Wu

2023-12-13