Featured Publications

Nature Methods (2024)

scGPT: toward building a foundation model for single-cell multi-omics using generative AI

Nature Methods (2022)

BIONIC: biological network integration using convolutions

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

Our paper is accepted by Nature Biotechnology!

March 1, 2024

Our paper Characterizing the impacts of dataset imbalance on single-cell data integration is accepted by Nature Biotechnology. We've thoroughly assessed how dataset imbalances can twist the results of single-cell integration and batch-correction, affecting everything from cell-type classification to differential expression. Our work doesn't just highlight the issue; it offers solid solutions!

scGPT is accepted by Nature Methods!

February 26, 2024

Our paper scGPT: toward building a foundation model for single-cell multi-omics using generative AI is accepted by Nature Methods. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities.

Our Nature commentary on gnerative AI on healthcare is out!

November 30, 2023

Generative AI could revolutionize health care — but not if control is ceded to big tech link

Our NeurIPS cell segmentation challenge is featured by Nature!

November 27, 2023

AI under the microscope: the algorithms powering the search for cells link

Congratulations on Dr. Bo Wang's appointment as the Chief AI Scientist at University Health Network!

September 20, 2023

Toronto hospital network appoints chief AI scientist in bid to improve health care. link

Congratulations to Dr. Bo Wang on being newly awarded Canada Research Chairs!

August 29, 2023

From building bone to children’s literacy: 36 U of T researchers awarded Canada Research Chairs. link

The Gloabl And Mail features our progress in AI for single-cell analysis!

August 1, 2023

Scientists are on a quest to map the human body cell by cell, charting where and why disease happens. link

Our Nature Method Commentary is out!

July 11, 2023

Towards foundation models of biological image segmentation link

Clinical Camel is released!

April 16, 2023

Clinical Camel is an ongoing project aimed at developing an open-source healthcare-focused chatbot. Demo link

Paper accepted as highlight by CVPR2023!

March 1, 2023

We introduce MAESTER - a new self-supervised method achieving STOA subcellular structure segmentation at pixel resolution! Press link

Our NeurIPS2022 Cell Segmentation Challenge is successfully concluded!

December 7, 2022

We had 400+ participants from all over the world. 1000+ submissions had been received in the leaderboard! The solutions from the winning team represent state-of-art methods for universal cell segmentation in both accuracy and efficiency. A summary paper will be out soon! Press link

Dr. Bo Wang is awarded with Gairdner Early Career Investigator Award!

October 5, 2022

Dr. Bo Wang was one of two winners selected by Drs. John Dick and Stuart Orkin. Dr. Wang was selected for his work on machine learning–based approaches to development trajectories of hematopoietic. Press link

BIONIC is accepted by Nature Methods!

October 3, 2022

Our paper BIONIC: biological network integration using convolutions is accepted by Nature Methods. BIONIC is a GNN (graph neural network)-based approach to integrating multiple biological networks to provide a holistic view of how genes/proteins interact in a cell. It is among the first DL methods that are capable of combining hundreds of large-scale biological networks.

Paper is accepted by Genome Biology

April 20, 2022

Our paper One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data is accepted by Genome Biology. OCAT is a fast and scalable scRNA-seq integration tool that preserves biological variations while being robust to heterogenous data compared to state-of-the-art methods, such as Seurat, Harmony, and Scanorama.

Medical Image Segementation Challenge at MICCAI2022!

April 2, 2022

We are excited to officially announce that the Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation challenge (FLARE22) will take place in MICCAI2022! Join the challenge now: Registration link!

Interviewd by The Gloabl And Mail

November 26, 2021

Dr. Wang was interviewd by The Gloabl And Mail on "How AI will change what happens when you see a doctor" Press link.

Inaugural Temerty Professor in AI Research and Education in Medicine

July 27, 2021

The Temerty Centre for AI Research and Education in Medicine (T-CAIREM) at the University of Toronto’s Temerty Faculty of Medicine announced Professor Bo Wang as the inaugural Temerty Professor in AI Research and Education in Medicine! Congratulations! Announcement link.

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!

Want to discuss a project?

Do you have ideas that you think may help us do a better science? Then...

Contact us