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Grant Award

Chao Zhang, PhD, Awarded $2.2M NIH Grant

Chao Zhang, PhD, assistant professor of medicine/computational biomedicine at Boston University Chobanian & Avedisian School of Medicine, has received a five-year, $2.2M R35 grant from the National Institutes of Health’s National Institute of General Medical Sciences for his project, “A Deep Learning Framework for Discovering Temporal Changes in Host-Microbiome Interactions.”

With this new grant, Zhang will develop novel artificial intelligence (AI) frameworks to investigate how host-microbiome interactions change over time.

With this new grant, Zhang will develop novel artificial intelligence (AI) frameworks to investigate how host-microbiome interactions change over time. The project aims to characterize dynamic immune cell recruitment, uncover mechanisms underlying drug resistance, and determine how the microbiome influences the host epigenome through longitudinal multi-omics data integration. Beyond developing cutting-edge AI methods, Zhang’s group works closely with physician-scientists to address clinically relevant challenges.

In collaboration with researchers at Weill Cornell Medicine, his team is investigating how local microbiomes interact with the host microenvironment to better understand upper gastrointestinal cancer development, progression, and early detection. In another project with physicians at Boston Medical Center, the team is exploring whether microbiome profiles can predict patient response to immune checkpoint inhibitor therapy. This work could ultimately support the development of rapid, non-invasive testing strategies to improve cancer treatment outcomes.

Zhang began his microbiome research during his doctoral training, developing computational approaches to study host-microbiome interactions. He established a generalizable pipeline for quantifying microbiomes from small biopsy samples, an approach that can be applied in many clinical settings where specimen availability is limited. This method also enables microbiome profiling from existing human genomics datasets that were not originally designed for microbiome analysis. In addition, his work has explored the bidirectional “microbiome-epigenome axis,” investigating interactions between the microbiomes and host epigenetic changes, including histone modifications, DNA methylation, and non-coding RNAs.

Zhang holds a PhD in computer science and an MS in statistics, and has extensive research experience in cancer, stem cell biology, and neurodegenerative diseases. His interdisciplinary background uniquely positions him to bridge systems biology, statistics, machine learning, software engineering, and next-generation sequencing analysis to address complex biomedical questions.