Portrait of Yue Li

Yue Li

Associate Academic Member
Assistant Professor, McGill University, School of Computer Science
Research Topics
Computational Biology

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

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

Publications

Abstract 4142894: Multimorbidity Trajectories Across the Lifespan in Patients with Congenital Heart Disease
Chao Li
Aihua Liu
Solomon Bendayan
Liming Guo
Judith Therrien
Robyn Tamblyn
Jay Brophy
Ariane Marelli
Background: Befitted from advances in medical care, patients with congenital heart disease (CHD) now survive to adulthood but face elevated… (see more) risks of both cardiac and non-cardiac complications. Understanding the trajectories of comorbidity development over a patient's lifespan is cornerstone to optimize care expected to improve long-term health outcomes. Research Aim: This study aims to investigate the temporal sequences and evolution of comorbidities in CHD patients across their lifespan. We hypothesize that multimorbidity trajectories in CHD patients are linked to CHD lesion severity and age at onset of specific comorbidities. Methods: Using the Quebec CHD database which comprised data in outpatient visits, hospitalization records and vital status from 1983 to 2017, we designed a longitudinal cohort study evaluating the development of 39 comorbidities coded using ICD-9/10. Temporal sequences were mapped using median age of onset. Associations between disease pairs were quantified by hazard ratios from Cox proportional hazard models adjusting for age, sex, genetic syndrome, competing risks of death, and taking into account the time-varying nature of the predictor diseases. Results: The cohort included 9,764 individuals with severe and 127,729 with non-severe CHD lesions. In severe CHD patients, most comorbidities developed between ages 25 and 40. Comorbidity progression began with childhood cardiovascular diseases, followed by systemic diseases such as diabetes, liver and kidney diseases, and advanced to heart failure and dementia in middle adulthood. In addition, mental disorders emerged in early adulthood and were associated with subsequent development of kidney diseases and dementia. Different trajectories were observed in non-severe CHD patients with 2-3 decades later disease onsets and non-differential onsets between cardiovascular and systemic complications (Figure). Conclusions: Distinct multimorbidity trajectories were observed in CHD patients by CHD lesion severity. In patients with severe CHD lesions, early systemic diseases significantly influenced subsequent complications. These findings highlight the need for well-timed surveillance guidelines and interventions to improve health outcomes.
scMoE: single-cell mixture of experts for learning hierarchical, cell-type-specific, and interpretable representations from heterogeneous scRNA-seq data
Michael Huang
ConvNTC: Convolutional neural tensor completion for predicting the disease-related miRNA pairs and cell-related drug pairs
Pei Liu
Xiao Liang
Jiawei Luo
MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling
Ruohan Wang
Zilong Wang
Ziyang Song
Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to understand diseases with unique subgroups a… (see more)nd enhance personalized medicine for patients. However, existing machine learning algorithms either focus on specific diseases for better interpretability or produce coarse-grained phenotype topics without considering nuanced disease patterns. In this study, we propose a guided topic model, MixEHR-Nest, to infer sub-phenotype topics from thousands of disease using multi-modal EHR data. Specifically, MixEHR-Nest detects multiple subtopics from each phenotype topic, whose prior is guided by the expert-curated phenotype concepts such as Phenotype Codes (PheCodes) or Clinical Classification Software (CCS) codes. We evaluated MixEHR-Nest on two EHR datasets: (1) the MIMIC-III dataset consisting of over 38 thousand patients from intensive care unit (ICU) from Beth Israel Deaconess Medical Center (BIDMC) in Boston, USA; (2) the healthcare administrative database PopHR, comprising 1.3 million patients from Montreal, Canada. Experimental results demonstrate that MixEHR-Nest can identify subphenotypes with distinct patterns within each phenotype, which are predictive for disease progression and severity. Consequently, MixEHR-Nest distinguishes between type 1 and type 2 diabetes by inferring subphenotypes using CCS codes, which do not differentiate these two subtype concepts. Additionally, MixEHR-Nest not only improved the prediction accuracy of short-term mortality of ICU patients and initial insulin treatment in diabetic patients but also revealed the contributions of subphenotypes. For longitudinal analysis, MixEHR-Nest identified subphenotypes of distinct age prevalence under the same phenotypes, such as asthma, leukemia, epilepsy, and depression. The MixEHR-Nest software is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Nest.
Cell ontology guided transcriptome foundation model
Xinyu Yuan
Zhihao Zhan
Zuobai Zhang
Manqi Zhou
Jianan Zhao
Boyu Han
Transcriptome foundation models (TFMs) hold great promises of deciphering the transcriptomic language that dictate diverse cell functions by… (see more) self-supervised learning on large-scale single-cell gene expression data, and ultimately unraveling the complex mechanisms of human diseases. However, current TFMs treat cells as independent samples and ignore the taxonomic relationships between cell types, which are available in cell ontology graphs. We argue that effectively leveraging this ontology information during the TFM pre-training can improve learning biologically meaningful gene co-expression patterns while preserving TFM as a general purpose foundation model for downstream zero-shot and fine-tuning tasks. To this end, we present **s**ingle **c**ell, **Cell-o**ntology guided TFM (scCello). We introduce cell-type coherence loss and ontology alignment loss, which are minimized along with the masked gene expression prediction loss during the pre-training. The novel loss component guide scCello to learn the cell-type-specific representation and the structural relation between cell types from the cell ontology graph, respectively. We pre-trained scCello on 22 million cells from CellxGene database leveraging their cell-type labels mapped to the cell ontology graph from Open Biological and Biomedical Ontology Foundry. Our TFM demonstrates competitive generalization and transferability performance over the existing TFMs on biologically important tasks including identifying novel cell types of unseen cells, prediction of cell-type-specific marker genes, and cancer drug responses.
TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction
Ziyang Song
Qincheng Lu
Mike He Zhu
In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (see more)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.
TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction
Ziyang Song
Qincheng Lu
Mike He Zhu
In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (see more)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.
TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
Ziyang Song
Qingcheng Lu
He Zhu
Cell ontology guided transcriptome foundation model
Xinyu Yuan
Zhihao Zhan
Zuobai Zhang
Manqi Zhou
Jianan Zhao
Boyu Han
Transcriptome foundation models (TFMs) hold great promises of deciphering the transcriptomic language that dictate diverse cell functions by… (see more) self-supervised learning on large-scale single-cell gene expression data, and ultimately unraveling the complex mechanisms of human diseases. However, current TFMs treat cells as independent samples and ignore the taxonomic relationships between cell types, which are available in cell ontology graphs. We argue that effectively leveraging this ontology information during the TFM pre-training can improve learning biologically meaningful gene co-expression patterns while preserving TFM as a general purpose foundation model for downstream zero-shot and fine-tuning tasks. To this end, we present **s**ingle **c**ell, **Cell**-**o**ntology guided TFM (scCello). We introduce cell-type coherence loss and ontology alignment loss, which are minimized along with the masked gene expression prediction loss during the pre-training. The novel loss component guide scCello to learn the cell-type-specific representation and the structural relation between cell types from the cell ontology graph, respectively. We pre-trained scCello on 22 million cells from CellxGene database leveraging their cell-type labels mapped to the cell ontology graph from Open Biological and Biomedical Ontology Foundry. Our TFM demonstrates competitive generalization and transferability performance over the existing TFMs on biologically important tasks including identifying novel cell types of unseen cells, prediction of cell-type-specific marker genes, and cancer drug responses. Source code and model weights are available at https://github.com/DeepGraphLearning/scCello.
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Shi Han
Yu Li
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Liam Hodgson
Yasser Iturria-Medina
Jo Anne Stratton
Smita Krishnaswamy
David A. Bennett
Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique
Manqi Zhou
Hao Zhang
Zilong Bai
Dylan Mann-Krzisnik
Fei Wang