Portrait of Yue Li

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

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

Postdoctorate - McGill University
PhD - McGill University
PhD - McGill University
Master's Research - McGill University
Master's Research - McGill University
Master's Research - McGill University
PhD - McGill University
Principal supervisor :
PhD - McGill University
Master's Research - McGill University
Principal supervisor :
PhD - McGill University
PhD - McGill University
Co-supervisor :
Master's Research - McGill University
Co-supervisor :
Master's Research - McGill University
Postdoctorate - McGill University
Co-supervisor :

Publications

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
Ariane Marelli
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. 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. 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. MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS
Publisher Correction: The default network of the human brain is associated with perceived social isolation
R. Nathan Spreng
Laetitia Mwilambwe-Tshilobo
Alain Dagher
Philipp Koellinger
Gideon Nave
Anthony Ong
Julius M. Kernbach
Thomas V. Wiecki
Tian Ge
Avram J. Holmes
B. T. Thomas Yeo
Gary R. Turner
Robin I. M. Dunbar
The default network of the human brain is associated with perceived social isolation
R. Nathan Spreng
Laetitia Mwilambwe-Tshilobo
Alain Dagher
Philipp Koellinger
Gideon Nave
Anthony Ong
Julius M. Kernbach
Thomas V. Wiecki
Tian Ge
Avram J. Holmes
B. T. Thomas Yeo
Gary R. Turner
Robin I. M. Dunbar
Humans survive and thrive through social exchange. Yet, social dependency also comes at a cost. Perceived social isolation, or loneliness, a… (see more)ffects physical and mental health, cognitive performance, overall life expectancy, and increases vulnerability to Alzheimer’s disease-related dementias. Despite severe consequences on behavior and health, the neural basis of loneliness remains elusive. Using the UK Biobank population imaging-genetics cohort (n = ~40,000, aged 40–69 years when recruited, mean age = 54.9), we test for signatures of loneliness in grey matter morphology, intrinsic functional coupling, and fiber tract microstructure. The loneliness-linked neurobiological profiles converge on a collection of brain regions known as the ‘default network’. This higher associative network shows more consistent loneliness associations in grey matter volume than other cortical brain networks. Lonely individuals display stronger functional communication in the default network, and greater microstructural integrity of its fornix pathway. The findings fit with the possibility that the up-regulation of these neural circuits supports mentalizing, reminiscence and imagination to fill the social void.
Global Surveillance of COVID-19 by mining news media using a multi-source dynamic embedded topic model.
Zhi Wen
Imane Chafi
Anya Okhmatovskaia
Guido Powell
David L. Buckeridge
As the COVID-19 pandemic continues to unfold, understanding the global impact of non-pharmacological interventions (NPI) is important for fo… (see more)rmulating effective intervention strategies, particularly as many countries prepare for future waves. We used a machine learning approach to distill latent topics related to NPI from large-scale international news media. We hypothesize that these topics are informative about the timing and nature of implemented NPI, dependent on the source of the information (e.g., local news versus official government announcements) and the target countries. Given a set of latent topics associated with NPI (e.g., self-quarantine, social distancing, online education, etc), we assume that countries and media sources have different prior distributions over these topics, which are sampled to generate the news articles. To model the source-specific topic priors, we developed a semi-supervised, multi-source, dynamic, embedded topic model. Our model is able to simultaneously infer latent topics and learn a linear classifier to predict NPI labels using the topic mixtures as input for each news article. To learn these models, we developed an efficient end-to-end amortized variational inference algorithm. We applied our models to news data collected and labelled by the World Health Organization (WHO) and the Global Public Health Intelligence Network (GPHIN). Through comprehensive experiments, we observed superior topic quality and intervention prediction accuracy, compared to the baseline embedded topic models, which ignore information on media source and intervention labels. The inferred latent topics reveal distinct policies and media framing in different countries and media sources, and also characterize reaction to COVID-19 and NPI in a semantically meaningful manner. Our PyTorch code is available on Github (htps://github.com/li-lab-mcgill/covid19_media).