Yue Li

Associate Academic Member

Assistant Professor, McGill University, School of Computer Science

Research Topics

AI in Health

Bayesian Models

Computational Biology

Deep Learning

Genetics

Large Language Models (LLM)

Multimodal Learning

Single-Cell Genomics

Website

Google Scholar

Biography

I completed my PhD degree in computer science and computational biology at the University of Toronto in 2014. Prior to joining McGill University, I was a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT (2015–2018).

In general, my research program covers three main research areas that involve applied machine learning in computational genomics and health. More specifically, it focuses on developing interpretable probabilistic learning models and deep learning models to model genetic, epigenetic, electronic health record and single-cell genomic data.

By systematically integrating multimodal and longitudinal data, I aim to have impactful applications in computational medicine, including building intelligent clinical recommender systems, forecasting patient health trajectories, making personalized polygenic risk predictions, characterizing multi-trait functional genetic mutations, and dissecting cell-type-specific regulatory elements that underpin complex traits and diseases in humans.

Current Students

Jun Bai

Postdoctorate - McGill University

Doruk Cakmakci

PhD - McGill University

Website

Github

Google Scholar

Vicky Dong

Master's Research - McGill University

Claris Gu

Master's Research - McGill University

Eric Huang

Master's Research - McGill University

Github

Yixuan Li

PhD - McGill University

Principal supervisor :

Archer Yang

Dylan Mann-Krzisnik

PhD - McGill University

Marshall Meng

Master's Research - McGill University

Principal supervisor :

Archer Yang

Adrien Osakwe

PhD - McGill University

Github

Google Scholar

Vishvak Raghavan

Master's Research - McGill University

Co-supervisor :

Jun Ding

Github

Wilbur Wang

Master's Research - McGill University

Mona Wang Wang

PhD - McGill University

Kunpeng Xu

Postdoctorate - McGill University

Co-supervisor :

Archer Yang

Shadi Zabad

PhD - McGill University

Github

Publications

Modeling electronic health record data using a knowledge-graph-embedded topic model

Yuesong Zou

Ahmad Pesaranghader

Aman Verma

David Buckeridge

Yue Li

The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (see more)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.

2022-06-03

ArXiv (preprint)

doi.org

arxiv.org

Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

Zhi Wen

Guido Powell

Imane Chafi

David Buckeridge

Y. K. Li

Yue Li

2022-02-01

Patterns (published)

doi.org

Supervised multi-specialist topic model with applications on large-scale electronic health record data

Ziyang Song

Xavier Sumba Toral

Yixin Xu

Aihua Liu

Liming Guo

Guido Powell

Aman Verma

David Buckeridge

Ariane Marelli

Yue Li

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision … (see more)medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. Materials and Methods: We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. Results: We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. Availability and implementation: MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS

2021-08-01

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (published)

doi.org

arxiv.org

Publisher Correction: The default network of the human brain is associated with perceived social isolation

R. Nathan Spreng

Emile Dimas

Laetitia Mwilambwe-Tshilobo

Alain Dagher

Philipp Koellinger

Gideon Nave

Anthony Ong