Portrait de Jun Ding

Jun Ding

Membre affilié
Professeur adjoint, McGill University, Département de médecine
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Biologie computationnelle

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

Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :

Publications

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
Hao Wu
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
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.
scSemiProfiler: Advancing Large-scale Single-cell Studies through Semi-profiling with Deep Generative Models and Active Learning
Jingtao Wang
Gregory Fonseca
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Shi Han
Yu Li
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
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.
An enhanced wideband tracking method for characteristic modes
Chao Huang
Chenjiang Guo
Xia Ma
Yi Yuan
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.
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
Maziar Divangahi
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
Maziar Divangahi
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
Maziar Divangahi
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
Maziar Divangahi
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
Maziar Divangahi
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 … (voir 2 de plus)
Naftali Kaminski
Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap rem… (voir plus)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.