Bin Chen , PhD
Biography
Bin was recruited to MSU through the Global Impact Initiative. Prior to this position, Bin was a faculty member (instructor, assistant professor) at UCSF for three years. Bin trained as a chemist in college, worked as a software engineer before graduate school, trained as a chem/bioinformatician in graduate school, worked as a computational scientist at Novartis, Pfizer and Merck. He received his PhD in informatics at Indiana University, Bloomington and pursued his postdoctoral training in Dr. Atul Butte's lab at Stanford University. Bin co-founded DahShu, a non-profit organization to promote research and education in data sciences. As a PI, he has received >$4.5 million research funding. He has also contributed to a number of big grants (e.g., P01 and U24) as a co-investigator.
Employment
Assistant Professor, Michigan State University, Grand Rpaids, 2018 - Present
Instructor/Assistant Professor, University of California, San Francisco, San Francisco, 2015 - 2018
Publications
Liver Metastasis Risk and Timing in Pancreatic Cancer Patients Using Electronic Health Records (2025)
Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization Briefings in Bioinformatics (2025)
STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics Nature Communications (2025)
Imputing abundance of over 2,500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles Cell Systems (2024)
TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning Genomics, Proteomics & Bioinformatics (2024)
Computational discovery of co-expressed antigens as dual targeting candidates for cancer therapy through bulk, single-cell, and spatial transcriptomics Bioinformatics Advances (2024)
OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features Nature Protocols (2021)
Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma Nature Reviews Gastroenterology & Hepatology (2020)
Publisher Correction: Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma Nature Reviews Gastroenterology & Hepatology (2020)