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
Site web
Google Scholar

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
Superviseur⋅e principal⋅e :
Yue Li
Github

Publications

scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization
Bowen Zhao
Kailu Song
Dong-Qing Wei
Yi Xiong
Jun Ding
2025-02-13
Communications Biology (publié)
doi.org
DTPSP: A Deep Learning Framework for Optimized Time Point Selection in Time-Series Single-Cell Studies
Michel Hijazin
Pumeng Shi
Jingtao Wang
Jun Ding
Time-series studies are critical for uncovering dynamic biological processes, but achieving comprehensive profiling and resolution across mu… (voir plus)ltiple time points and modalities (multi-omics) remains challenging due to cost and scalability constraints. Current methods for studying temporal dynamics, whether at the bulk or single-cell level, often require extensive sampling, making it impractical to deeply profile all time points and modalities. To overcome these limitations, we present DTPSP, a deep learning framework designed to identify the most informative time points in any time-series study, enabling resource-efficient and targeted analyses. DTPSP models temporal gene expression patterns using readily obtainable data, such as bulk RNA-seq, to select time points that capture key system dynamics. It also integrates a deep generative module to infer data for non-sampled time points based on the selected time points, reconstructing the full temporal trajectory. This dual capability enables DTPSP to prioritize key time points for in-depth profiling, such as single-cell sequencing or multi-omics analyses, while filling gaps in the temporal landscape with high fidelity. We apply DTPSP to developmental and disease-associated time courses, demonstrating its ability to optimize experimental designs across bulk and single-cell studies. By reducing costs, enabling strategic multi-omics profiling, and enhancing biological insights, DTPSP provides a scalable and generalized solution for investigating dynamic systems.
2024-12-20
bioRxiv (prépublication)
doi.org
DTPSP: A Deep Learning Framework for Optimized Time Point Selection in Time-Series Single-Cell Studies
Michel Hijazin
Pumeng Shi
Jingtao Wang
Jun Ding
2024-12-20
bioRxiv (prépublication)
doi.org
CellSexID: Sex-Based Computational Tracking of Cellular Origins in Chimeric Models
Huilin Tai
Qian Li
Jingtao Wang
Jiahui Tan
Ryann Lang
Basil J. Petrof
Jun Ding
Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplanta… (voir plus)tion studies. Existing methods, such as fluorescent labeling and genetic barcoding, are technically demanding, costly, and often impractical for dynamic, heterogeneous tissues. To address these limitations, we propose a computational framework that leverages sex as a surrogate marker for cell tracking. Our approach uses a machine learning model trained on single-cell transcriptomic data to predict cell sex with high accuracy, enabling clear distinction between donor (male) and recipient (female) cells in sex-mismatched chimeric models. The model identifies specific genes critical for sex prediction and has been validated using public datasets and experimental flow sorting, confirming the biological relevance of the identified cell populations. Applied to skeletal muscle macrophages, our method revealed distinct transcriptional profiles associated with cellular origins. This pipeline offers a robust, cost-effective solution for cell tracking in chimeric models, advancing research in regenerative medicine and immunology by providing precise insights into cellular origins and therapeutic outcomes.
2024-12-05
bioRxiv (prépublication)
doi.org
CellSexID: Sex-Based Computational Tracking of Cellular Origins in Chimeric Models
Huilin Tai
Qian Li
Jingtao Wang
Jiahui Tan
Ryann Lang
Basil J. Petrof
Jun Ding
Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplanta… (voir plus)tion studies. Existing methods, such as fluorescent labeling and genetic barcoding, are technically demanding, costly, and often impractical for dynamic, heterogeneous tissues. To address these limitations, we propose a computational framework that leverages sex as a surrogate marker for cell tracking. Our approach uses a machine learning model trained on single-cell transcriptomic data to predict cell sex with high accuracy, enabling clear distinction between donor (male) and recipient (female) cells in sex-mismatched chimeric models. The model identifies specific genes critical for sex prediction and has been validated using public datasets and experimental flow sorting, confirming the biological relevance of the identified cell populations. Applied to skeletal muscle macrophages, our method revealed distinct transcriptional profiles associated with cellular origins. This pipeline offers a robust, cost-effective solution for cell tracking in chimeric models, advancing research in regenerative medicine and immunology by providing precise insights into cellular origins and therapeutic outcomes.
2024-12-05
bioRxiv (prépublication)
doi.org
MATES: A Deep Learning-Based Model for Locus-specific Quantification of Transposable Elements in Single Cell
Ruohan Wang
Yumin Zheng
Zijian Zhang
Kailu Song
Erxi Wu
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.
2024-10-11
Nature Communications (publié)
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
2024-07-31
Communications Biology (publié)
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.
2024-07-29
Genome Biology (publié)
doi.org
scSemiProfiler: Advancing Large-scale Single-cell Studies through Semi-profiling with Deep Generative Models and Active Learning
Jingtao Wang
Gregory Fonseca
Jun Ding
2024-07-16
Nature Communications (publié)
doi.org
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Shi Han
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