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
Web: DTF: Deep tensor factorization for predicting anticancer drug synergy
Description: A new algorithm integrating tensor factorization and deep learning to predict anticancer drug combinations
Citation: Sun et al. Bioinformatics, In Press
Web: An OpenMP based tool for finding LCS of DNA sequence data
Description: 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.
Resources
Deep Learning
Data sets
Stanford Biomedical Network Dataset Collection
Human Pathology Proteome - The Human Protein Atlas
Genomics of Drug Sensitivity in Cancer
Deep Learning in Medicine and Biology: Data and Tools and here
cBioportal for Cancer Genomics
Pretrained Deep Learning Models for Bioinformatics
Deep Learning for Drug Discovery
Tools
Others
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