Yue Li

Alain Han

Baccalauréat - McGill

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

Ziyang Song

Doctorat - McGill

Wilbur Wang

Maîtrise recherche - McGill

Shadi Zabad

Doctorat - McGill

Caiya Zhang

Maîtrise recherche - McGill

He Zhu

Doctorat - McGill

Publications

Multi-ancestry polygenic risk scores using phylogenetic regularization

Elliot Layne

Shadi Zabad

Mathieu Blanchette

2024-02-17

bioRxiv (prépublication)

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 (publié)

MiRGraph: A transformer-based feature learning approach to identify microRNA-target interactions by integrating heterogeneous graph network and sequence information

Pei Liu

Ying Liu

Jiawei Luo

MicroRNAs (miRNAs) play a crucial role in the regulation of gene expression by targeting specific mRNAs. They can function as both tumor sup… (voir plus)pressors and oncogenes depending on the specific miRNA and its target genes. Detecting miRNA-target interactions (MTIs) is critical for unraveling the complex mechanisms of gene regulation and identifying therapeutic targets and diagnostic markers. There is currently a lack of MTIs prediction method that simultaneously performs feature learning on heterogeneous graph network and sequence information. To improve the prediction performance of MTIs, we present a novel transformer-based multi-view feature learning method, named MiRGraph. It consists of two main modules for learning the sequence and heterogeneous graph network, respectively. For learning the sequence-based feaature embedding, we utilize the mature miRNA sequence and the complete 3’UTR sequence of the target mRNAs to encode sequence features. Specifically, a transformer-based CNN (TransCNN) module is designed for miRNAs and genes respectively to extract their personalized sequence features. For learning the network-based feature embedding, we utilize a heterogeneous graph transformer (HGT) module to extract the relational and structural information in a heterogeneous graph consisting of miRNA-miRNA, gene-gene and miRNA-target interactions. We learn the TransCNN and HGT modules end-to-end by utilizing a feedforward network, which takes the combined embedded features of the miRNA-gene pair to predict MTIs. Comparisons with other existing MTIs prediction methods illustrates the superiority of MiRGraph under standard criteria. In a case study on breast cancer, we identified plausible target genes of an oncomir hsa-MiR-122-5p and plausible miRNAs that regulate the oncogene BRCA1.

2024-01-26

bioRxiv (prépublication)

Bidirectional Generative Pre-training for Improving Time Series Representation Learning

Ziyang Song

Qincheng Lu

He Zhu

David Buckeridge

2024-01-01

arXiv.org (prépublication)

arxiv.org

Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting

Ziyang Song

Qincheng Lu

Hao Xu

He Zhu

David Buckeridge

2023-11-29

ArXiv (prépublication)

arxiv.org

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 (prépublication)

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 (publié)

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… (voir plus)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 (prépublication)

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

Chang Su

Yu 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

Fei Wang

2023-04-01

iScience (publié)

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

Fei Wang

The advent of single-cell multi-omics sequencing technology makes it possible for re-searchers to leverage multiple modalities for individua… (voir plus)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 (prépublication)

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

Yuesong Zou

Ahmad Pesaranghader

Ziyang Song

Aman Verma

David Buckeridge