Dr. Jun Wan

Address:

Research Interests:

Dr. Wan's research focuses on the computational and functional analysis of biological networks:

• Bioinformatics And Computational Systems Biology
• Epigenetics And Gene Regulation
• Cancer Genomics And Translational Informatics
• Multi-Omics Data Integration And Analysis
• Deep Learning And Data Mining For Genetics

Biography:

Dr. Jun Wan is an accomplished scientist specializing in Bioinformatics and Computational Systems Biology, with a core focus on Cancer Genomics. His research aims to understand the functional alterations of gene regulatory networks from the perspectives of epigenetics, transcription, and translation. He employs Deep Learning and Data Mining methods to decipher genetic and epigenetic codes, investigate master regulators for gene programming, and analyze small molecule-protein interactions. His work is critical for advancing personalized medicine and discovering new therapeutic targets in cancer research.

Education Details

Dr. Wan's academic background includes an unusual but impactful pivot from physics to biomedical research:

• PhD: Physics (Queen's University, Kingston, Ontario, Canada, 2006)
• Postdoctoral Training: Bioinformatics (Johns Hopkins University School Of Medicine, Baltimore, MD)
• M.S.: Physics (Fudan University, China, 2001)
• B.S.: Physics (Shanghai Jiao Tong University, China, 1991)
   

Professional Background

His professional career includes leadership in bioinformatics core facilities at major NCI-designated cancer centers:

• Associate Professor With Tenure (Current, Since 2022) - Indiana University School Of Medicine.
• Director (Current) - Collaborative Core For Cancer Bioinformatics (C3B), serving Indiana University Simon Comprehensive Cancer Center (IUSCCC) and Purdue University Center for Cancer Research (PUCCR).

Achievements

Dr. Wan has received recognition for his influential research in computational systems biology:

• Showalter Scholar (Awarded in 2023).
• Director of the Collaborative Core For Cancer Bioinformatics (C3B).
   

Current Research Projects

His current research efforts are focused on integrating multi-omics data and developing advanced computational methods for cancer:

• Multi-Omics Integration: Developing computational frameworks (like DiCE) to integrate diverse '-omics' data and identify subtype-specific predictors of drug response, particularly in breast cancer.
• Gene Regulatory Networks: Exploring master regulators and their interactions through DNA methylation, microRNA regulation, and chromatin organization studies.
   

Academic Profiles Of Dr. Jun Wan

Explore his academic and professional presence across trusted platforms:

• Google Scholar
• University Faculty Profile
• Wan Lab Homepage
• Web of Science

Publications:

Dr. Wan's publications feature significant work in computational methodology and the molecular biology of cancer and gene regulation, often in high-impact journals. The list of recent publications of Dr. Jun Wan is listed below:
 

1. Xu, F., Liang, C., Wang, K., Mo, Z., Xu, Z., Zhu, Z., Shang, H., Chen, X., Wan, J., et al. (2025). Multi-omics integration reveals subtype-specific predictors of neoadjuvant treatment response in breast cancer. Science Advances. DOI: 10.1126/sciadv.adu1521.
2. Pashaei, E., Liu, S., Li, K., Zang, Y., Yang, L., Lautenschlaeger, T., Huang, J., Lu, X., & Wan, J. (2025). DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein–protein interaction network. Nucleic Acids Research, 53(13). DOI: 10.1093/nar/gkaf609.
3. Cosper, P. F., Hrycyniak, L. C. F., Lasek, A. L., Liu, Y., Wan, J., et al. (2023). The ARF tumor suppressor prevents chromosomal instability and ensures mitotic checkpoint fidelity through regulation of Aurora B. Proceedings of the National Academy of Sciences, 120(14), e2216700120. DOI: 10.1073/pnas.2216700120.
4. Hrycyniak, L. C. F., Cosper, P. F., Paracha, M., Lasek, A. L., Liu, Y., Wan, J., et al. (2023). The ARF tumor suppressor prevents chromosomal instability and ensures mitotic checkpoint fidelity through regulation of Aurora B. Journal of Biological Chemistry, 298(7), 102123. DOI: 10.1016/j.jbc.2022.102123.
5. Wu, A. P., Singh, R., Walsh, C., & Wan, J. (2023). Unveiling causal regulatory mechanisms through cell-state parallax. Nature Communications, 14(1), 7765. DOI: 10.1038/s41467-023-43360-1.

Last Updated on September 30, 2025