Yue Li

Membre académique associé

Professeur adjoint, McGill University, École d'informatique

Sujets de recherche

Apprentissage multimodal

Apprentissage profond

Biologie computationnelle

Génétique

Génomique unicellulaire

Grands modèles de langage (LLM)

IA en santé

Modèles bayésiens

Site web

Google Scholar

Biographie

J'ai obtenu un doctorat en informatique et biologie computationnelle de l'Université de Toronto en 2014. Avant de me joindre à l’Université McGill, j'ai été associé postdoctoral au Computer Science and Artificial Intelligence Laboratory (CSAIL) du Massachusetts Institute of Technology (MIT) (2015-2018).

Mes recherches portent sur le développement de modèles d'apprentissage probabilistes interprétables et de modèles d'apprentissage profond pour modéliser les données génétiques et épigénétiques, les dossiers de santé électroniques et les données génomiques unicellulaires.

En intégrant systématiquement des données multimodales et longitudinales, je cherche à obtenir des applications qui auront des effets tangibles en médecine computationnelle, y compris la construction de systèmes de recommandation clinique intelligents, la prévision des trajectoires de santé des patients, les prédictions personnalisées de risques polygéniques, la caractérisation des mutations génétiques fonctionnelles multitraits, et la dissection des éléments réglementaires spécifiques au type de cellule qui sont à la base des traits complexes et des maladies chez l'homme. Mon programme de recherche couvre trois domaines principaux impliquant l'apprentissage automatique appliqué à la génomique computationnelle et à la santé.

Étudiants actuels

Jun Bai

Postdoctorat - McGill

Doctorat - McGill

Vicky Dong

Maîtrise recherche - McGill

Claris Gu

Maîtrise recherche - McGill

Eric Huang

Maîtrise recherche - McGill

Github

Yixuan Li

Doctorat - McGill

Superviseur⋅e principal⋅e :

Archer Yang

Dylan Mann-Krzisnik

Doctorat - McGill

Marshall Meng

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Maîtrise recherche - McGill

Co-superviseur⋅e :

Jun Ding

Github

Wilbur Wang

Maîtrise recherche - McGill

Mona Wang Wang

Doctorat - McGill

Kunpeng Xu

Postdoctorat - McGill

Co-superviseur⋅e :

Archer Yang

Shadi Zabad

Doctorat - McGill

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… (voir plus)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 (prépublication)

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 (publié)

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 … (voir plus)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 (publié)

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