Our Research
Deep learning-driven drug design and drug discovery
Deep learning - driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles.
C Liu, AM Hogan, H Sturm, MW Khan, MM Islam, ASMZ Rahman, R Davis, ST Cardona, P Hu.
Journal of Cheminformatics, 14:12,2022.
DTF: Deep tensor factorization for predicting anticancer drug synergy
Z Sun, S Huang, P Jiang, P Hu
Bioinformatics. 36:4483-4489, 2020.
Machine learning-empowered radiomic and radiogenomics
YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms
Y Su, Q Liu, W Xie, P Hu.
Computer Methods and Programs in Biomedicine,221:106903, 2022.
An integrative computational framework for breast cancer radiogenomics biomarker discovery
Q Liu, P Hu.
Computational and Structural Biotechnology Journal, 20:2484-2494, 2022.
Model-based multiomic (multimodal) data integration
Tightly integrated multiomics-based deep tensor survival model for time-to-event prediction
Z Zhang, W Xu, P Hu.
Bioinformatics, 38:3259-3266, 2022.
Bayesian tensor factorization-driven breast cancer subtyping by integrating multi-omics data
Q Liu, B Cheng, Y Jin, P Hu.
Journal of Biomedical Informatics. 125:103958, 2022.
Machine learning based spatial transcriptomics and single cell genomics
scGMM-VGAE: A Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data
E Lin, B Liu, L Lac, DLX Fung, CK Leung, P Hu.
Machine Learning: Science and Technology. 4 035013 , 2023
ChrNet: A re-trainable chromosome-based 1D convolutional neural network for predicting immune cell types.
S Jia P Hu
Genomics. 113:2023-2031, 2021.
Collaborative research
Dr. Hu has outstanding experience in collaborating with basic scientists and clinicians. He was one of the major drivers to establish and develop the first statistical facility for omics data analysis (https://tcag.ca/facilities/statisticalAnalysis.html) in Canada when he acted as the facility’s manager in The Centre for Applied Genomics at Sickkids, Toronto. In this position, he consulted for and collaborated with more than 200 national and international basic and clinician scientists.
In the past 9 years, as a health data science lead, Dr. Hu has helped other principal investigators successfully apply for six CIHR project and team grants, and as a nominated principal investigator, Dr. Hu successfully applied for 2 CIHR project grants in human/statistical genetics, microbiome, methylation, proteomics, chemogenetics and single cell analysis.
Our Funding Partners
