Bin Chen, PhD

Associate Professor
Pediatrics & Human Development, Pharmacology & Toxicology

Bio

I am a tenured associate professor leading a multidisciplinary lab, with a mission to leverage advanced machine learning and emerging big data to discover new therapeutics. I am also the Founding PI of the Center for AI-Enabled Drug Discovery in the College of Human Medicine at MSU. My current research areas include machine learning method development, integrative bioinformatics, and EHR mining. I have training in informatics, chemistry, and biology, and working experience in big pharmaceutical companies and small startups. As a corresponding author, I have published in many high-profile journals such as Cell, Nature Protocols, Nature Reviews Gastroenterology & Hepatology, Nature Communications, Cell Systems, Genomics, Proteomics & Bioinformatics, and Briefings in Bioinformatics. As a PI/co-PI, I have received a K01 career training award, multiple R01-level NIH grants, industry-sponsored grants, and foundation grants, totaling over $7 million. I have served as a mentor/co-mentor for two K99 training grants and have mentored four trainees who have gone on to become PIs. In the next few years, I aim to pioneer transcriptomics-based drug discovery, develop foundational models to understand how individual cells respond to perturbations, and utilize massive real-world data to assess drug efficacy.

Colleges

• College of Human Medicine

Programs

• Integrated Pharmacological Sciences Training Program
• Doctor of Philosophy in Pharmacology and Toxicology

Works

• Liver Metastasis Risk and Timing in Pancreatic Cancer Patients Using Electronic Health Records. (2025-11-02)
• Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization. Briefings in Bioinformatics (2025-05-01)
• STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics. Nature Communications (2025-02-20)
• Comparative immuno-biology at clinical recognition of early multiple organ dysfunction syndrome in pediatric and adult patients using single-cell transcriptomics. (2025-01-02)
• Imputing abundance of over 2,500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles. Cell Systems (2024)
• Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles. (2024-07-31)
• TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning. Genomics, Proteomics & Bioinformatics (2024-07-03)
• Computational discovery of co-expressed antigens as dual targeting candidates for cancer therapy through bulk, single-cell, and spatial transcriptomics. Bioinformatics Advances (2024-01-05)
• OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features. Nature Protocols (2021-02-23)
• Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma. Nature Reviews Gastroenterology & Hepatology (2020-04-15)
• Publisher Correction: Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma. Nature Reviews Gastroenterology & Hepatology (2020-03-11)
• Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types. Nature Communications (2019-08-08)
• Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data. Nature Communications (2019-05-15)

Employment

• ZZ1Assistant/Associate Professor, ZZ2Michigan State University ZZ3Grand Rpaids ZZ$grid.17088.36 (2018-04-01)
• ZZ1Instructor/Assistant Professor, ZZ2University of California, San Francisco ZZ3San Francisco ZZ$grid.266102.1 (2015-04-01—2018-03-31)