Yue Li

Google Scholar

Vicky Dong

Master's Research - McGill University

Claris Gu

Master's Research - McGill University

jia.gu@mail.mcgill.ca

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

Google Scholar

Vishvak Raghavan

Master's Research - McGill University

Co-supervisor :

Jun Ding

Ziyang Song

PhD - McGill University

Zilong Wang

Collaborating Alumni - McGill University

Google Scholar

Wilbur Wang

Master's Research - McGill University

Shadi Zabad

PhD - McGill University

Caiya Zhang

Master's Research - McGill University

He Zhu

PhD - McGill University

Publications

Multi-ancestry polygenic risk scores using phylogenetic regularization

Elliot Layne

Shadi Zabad

Mathieu Blanchette

2024-02-17

bioRxiv (preprint)

Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning

Ziyang Song

Qincheng Lu

He Zhu

Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in … (see more)the healthcare domain. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on biosignals and longitudinal clinical records by both next-token and previous-token prediction in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignals and longitudinal clinical records, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from biosignal time-series sequences, even more so after fine-tuning on the task.

2024-02-14

ArXiv (preprint)

Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source

Ariane Marelli

Chao Li

Aihua Liu

Hanh Nguyen

Harry Moroz

James M. Brophy

Liming Guo

2024-02-01

JACC: Advances (published)

Bidirectional Generative Pre-training for Improving Time Series Representation Learning

Ziyang Song

Qincheng Lu

Mike He Zhu

2024-01-01

arXiv.org (preprint)

MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records

Yixuan Li

Ariane Marelli

Archer Yang

Survival models can help medical practitioners to evaluate the prognostic importance of clinical variables to patient outcomes such as morta… (see more)lity or hospital readmission and subsequently design personalized treatment regimes. Electronic Health Records (EHRs) hold the promise for large-scale survival analysis based on systematically recorded clinical features for each patient. However, existing survival models either do not scale to high dimensional and multi-modal EHR data or are difficult to interpret. In this study, we present a supervised topic model called MixEHR-SurG to simultaneously integrate heterogeneous EHR data and model survival hazard. Our contributions are three-folds: (1) integrating EHR topic inference with Cox proportional hazards likelihood; (2) integrating patient-specific topic hyperparameters using the PheCode concepts such that each topic can be identified with exactly one PheCode-associated phenotype; (3) multi-modal survival topic inference. This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality. We evaluated MixEHR-SurG using a simulated dataset and two real-world EHR datasets: the Quebec Congenital Heart Disease (CHD) data consisting of 8211 subjects with 75,187 outpatient claim records of 1767 unique ICD codes; the MIMIC-III consisting of 1458 subjects with multi-modal EHR records. Compared to the baselines, MixEHR-SurG achieved a superior dynamic AUROC for mortality prediction, with a mean AUROC score of 0.89 in the simulation dataset and a mean AUROC of 0.645 on the CHD dataset. Qualitatively, MixEHR-SurG associates severe cardiac conditions with high mortality risk among the CHD patients after the first heart failure hospitalization and critical brain injuries with increased mortality among the MIMIC-III patients after their ICU discharge. Together, the integration of the Cox proportional hazards model and EHR topic inference in MixEHR-SurG not only leads to competitive mortality prediction but also meaningful phenotype topics for in-depth survival analysis. The software is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-SurG.

2023-12-20

ArXiv (preprint)

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare

Ziyang Song

Qincheng Lu

Hao Xu

Mike He Zhu

2023-11-29

ArXiv (preprint)

Differential Chromatin Architecture and Risk Variants in Deep Layer Excitatory Neurons and Grey Matter Microglia Contribute to Major Depressive Disorder

Anjali Chawla

Doruk Cakmakci

Wenmin Zhang

Malosree Maitra

Reza Rahimian

Haruka Mitsuhashi

MA Davoli

Jenny Yang

Gary Gang Chen

Ryan Denniston

Deborah Mash

Naguib Mechawar

Matthew Suderman

Corina Nagy

Gustavo Turecki

2023-10-03

bioRxiv (preprint)

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes

Lakshmipuram Seshadri Swapna

Michael Huang

2023-08-18

Genome Biology (published)

Guided-topic modelling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes

Lakshmipuram Seshadri Swapna

Michael Huang

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infe… (see more)r cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data as a guide to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.

2023-07-03

bioRxiv (preprint)

Biomedical discovery through the integrative biomedical knowledge hub (iBKH).

Chang Su

Yufang Hou

Manqi Zhou

Suraj Rajendran

Jacqueline R.M. A. Maasch

Zehra Abedi

Haotan Zhang

Zilong Bai

Anthony Cuturrufo

Winston Guo

Fayzan F. Chaudhry

Gregory Ghahramani

Jian Tang

Feixiong Cheng

Rui Zhang

Steven T. DeKosky

Jiang Bian

Yi Wang

2023-04-01

iScience (published)

Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

Manqi Zhou

Hao Zhang

Zilong Bai

Dylan Mann-Krzisnik

Yi Wang

The advent of single-cell multi-omics sequencing technology makes it possible for re-searchers to leverage multiple modalities for individua… (see more)l cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human bone marrow mononuclear cells (BMMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

2023-01-31

bioRxiv (preprint)

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

Yuesong Zou

Ahmad Pesaranghader

Ziyang Song

Aman Verma

2022-10-25

Scientific Reports (published)