The Hu Lab

Bioinformatics and Statistical Genetics

My research program focuses on developing and applying artificial intelligence (AI, e.g. novel deep learning tools) and large-scale statistical techniques for integrative analysis of big multimodal health data (omics data, imaging data, administrative and electronic medical records) for precision medicine. My group also collaborates very closely with local, national and international life science scientists and clinicians on different omics projects. We are actively working in the following four research areas.

- Deep Learning and Visual Analytics Algorithms for  Omics Data, Medical Imaging and Electronic Medical Records

- Deep Learning  for  Drug Discovery

- Integrative Analysis Frameworks for Omics Data, Medical Imaging and Electronic Medical Records

- Translational Medicine and Precision Health

Software Development and Resources

Software Tools Developed in The Hu Lab

An OpenMP based tool for finding LCS of DNA sequence data. This repository contains three parallel implementation of the LCS algorithm in MPI, OpenMP, and hybrid MPI-OpenMP platforms.  Citation: Shikder et al. 2019, BMC Research Notes.

RecurrentCNV: A graph-based tool to recall recurrent copy number variations. Citations:  Kanwar et al. 2015, International Journal of Cancer and Chi et al. 2016, Cancer Informatics.


Deep Learning

Graph Neural Network

Generative adversarial network

Data sets

Allen Brain Atlases


Stanford Biomedical Network Dataset Collection

Human Pathology Proteome - The Human Protein Atlas

Genomics of Drug Sensitivity in Cancer

TCIA Collection

Deep Learning in Medicine and Biology: Data and Tools and here

cBioportal for Cancer Genomics

Microbiome and Metagenomics

Pretrained Deep Learning Models for Bioinformatics


Deep Learning for Drug Discovery

Rare Diseases


IDs transformation

List of genetic simulators


3rd Floor BMSB Card Access Request

Winnipeg Transit Planner

341 BMSB Booking System

Journal Selection