Pingzhao Hu

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Associate Professor

Western University

Canada

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About

Dr. Pingzhao Hu is an Associate Professor at Western University, Canada. His research areas include computational biology, drug-target interactions, and machine learning applications in cancer genomics. Recent publications involve advanced methodologies such as deep learning frameworks for molecular representation, predictive modeling for compound-protein interactions, and innovative approaches to spatial transcriptomics.

Recent Grants

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Manitoba-based breast cancer screening mammography using artificial intelligence ($150,000 CAD)

Open Date: 2020-07-01

Close Date: 2022-06-01

Grant: Close

Artificial intelligence and genomics-driven precision oncology for breast cancer ($85,000 CAD)

Open Date: 2020-07-01

Close Date: 2023-06-01

Grant: Close

Epigenetic PowerBlade: DNA Precision Marks for Critical Illness

Open Date: 2020-05-01

Close Date: 2023-04-01

Grant: Close

Epigenetic PowerBlade: DNA Precision Marks for Critical Illness ($416,250 CAD)

Open Date: 2020-05-01

Close Date: 2023-04-01

Grant: Close

Identification of novel antibiotic molecules by chemogenetic analysis and machine learning

Open Date: 2020-02-01

Close Date: 2025-02-01

Articles (18)

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2022

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2022

Collaborators (5)

Silvia T. Cardona

University of Manitoba

CANADA

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A. S. M. Zisanur Rahman

Teaching Assistant

University of Manitoba

CANADA

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Qian Liu

University of Winnipeg

CANADA

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Prashen Chelikani

Professor

University of Manitoba

CANADA

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Carson Leung

University of Manitoba

CANADA

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Pingzhao Hu

Research Interests

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About

Recent Grants

Articles (18)

MolGraph-xLSTM as a graph-based dual-level xLSTM framework for enhanced molecular representation and interpretability

GraphBAN: An inductive graph-based approach for enhanced prediction of compound-protein interactions

ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning

Computational frameworks integrating deep learning and statistical models in mining multimodal omics data

iNGNN-DTI: prediction of drug–target interaction with interpretable nested graph neural network and pretrained molecule models

Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer

Investigating alignment-free machine learning methods for HIV-1 subtype classification

Conditional probabilistic diffusion model driven synthetic radiogenomic applications in breast cancer

scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data

Bioinformatics driven discovery of small molecule compounds that modulate the FOXM1 and PPARA pathway activities in breast cancer

A self-knowledge distillation-driven CNN-LSTM model for predicting disease outcomes using longitudinal microbiome data

Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer

Association between Downstream Taste Signaling Genes, Oral Microbiome, and Severe Early Childhood Caries

Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles

Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry

A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery

Semi-supervised COVID-19 CT image segmentation using deep generative models

Tightly integrated multiomics-based deep tensor survival model for time-to-event prediction

Collaborators (5)

Silvia T. Cardona

A. S. M. Zisanur Rahman

Qian Liu

Prashen Chelikani

Carson Leung

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