Featured Publications

Lancet Digital Health (2020)

Genotyping SARS-CoV-2 through an interactive web application

Nature Communications (2018) 9.1: 3108

Network Enhancement: a general method to denoise weighted biological networks

Nature Methods

Nature Methods (2017) 14: 414

Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning

Nature Methods

Nature Methods (2014) 11: 333

Similarity network fusion for aggregating data types on a genomic scale

Recent News

Paper is accepted by Lancet Digital Health

April 12, 2021

Our paper Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data is accepted by Lancet Digital Health. We have developed and validated an innovative deep learning model to predict a patient's long-term outcome after receiving a liver transplant with over 80% accuracy.

Paper is accepted by Genome Biology

March 04, 2021

Our paper simATAC: a single-cell ATAC-seq simulation framework is accepted by Genome Biology. simATAC is a framework provided as an R package that generates a single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq) count matrix, highly resembling real scATAC-seq datasets in library size, sparsity, and averaged chromatin accessibility signals. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with cell labels for a comprehensive tool assessment.

Paper is accepted by Lancet Digital Health

June 12, 2020

Our paper Genotyping SARS-CoV-2 through an interactive web application is accepted by Lancet Digital Health. We provide the COVID-19 Genotyping Tool (CGT), which offers an online, user-friendly platform where researchers can compare the genome sequence of the SARS-CoV-2 virus in their hospital against the global picture.

Grant is accepted by CIFAR

June 10, 2020

A two-year grant DeepCell: analyze and integrate spatial single-cell RNA-seq data is accepted by CIFAR.

Paper is accepted by MICCAI

May 19, 2020

Our paper SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation is accepted by MICCAI 2020.

Paper is accepted by MICCAI

May 19, 2020

Our paper CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction is accepted by MICCAI 2020.

Grant is accepted by NSERC

May 19, 2020

A five-year grant Integrative analysis of single-cell multi-omics data with interpretable deep learning methods is accepted by NSERC.

Launch CiteNet Website

March 17, 2020

We launch the CiteNet website, a search engine designed for literature exploration.

Oral paper of AAAI 2020

November 11, 2019

Our paper Diversity Transfer Network for Few-Shot Learning is accepted by AAAI 2020 for oral presentation!

Oral paper of ICCV 2019

July 22, 2019

Our paper Moment Matching for Multi-Source Domain Adaptation is accepted by ICCV 2019 for oral presentation!

PMCC Innovation Award

June 1, 2019

Chris Mcintosh and Bo Wang won the PMCC Innovation Award for their proposal of an AI-based automatic coronary artery interpretation system. Congratulations!

Presentation: Artificial Intelligence for Cardiology

April 13, 2019

Bo Wang delivered his keynote presentation Artificial Intelligence for Cardiology at the Ottawa Heart Institute, Ottawa

Presentation: Integrative Network Analysis for Single-cell RNA-seq and Beyond

April 11, 2019

Bo Wang was invited to present his work “Integrative Network Analysis for Single-cell RNA-seq and Beyond” at the National Research Council, Ottawa.

CIFAR AI Chairs

April 8, 2019

Bo Wang is named as one of the CIFAR AI Chairs! Congratulations!

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