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Jianhua Ruan

FULL NAME 
Jianhua Ruan
CURRENT POSITION & AFFILIATION 
Professor, Department of Computer Science
INSTITUTION / ORGANIZATION
University of Texas at San Antonio

 

EMAIL ADDRESS
Jianhua.Ruan@utsa.edu 
PHONE NUMBER
210-493-7528
LINKEDIN PROFILE 
www.linkedin.com/in/jianhua-ruan-9589a8a

 

SECTION 2: PROFESSIONAL BIOGRAPHY  

Please provide a summary of your professional background and career journey. 

SUMMARY 
Dr. Jianhua Ruan is a Professor of Computer Science at the University of Texas at San Antonio and holds an adjunct appointment in the Department of Molecular Medicine at UT Health San Antonio. His career has been shaped by a commitment to building bridges between computer science, biology, and biomedical research. Trained first as a biologist and later as a computer scientist (B.S. in Biology, University of Science and Technology of China; Ph.D. in Computer Science and Engineering, Washington University in St. Louis), he brings together these disciplines to develop computational, statistical, and machine learning methods that advance biological discovery and disease research. His work spans interpretable machine learning, highdimensional omics data analysis, and gene regulatory network modeling, supported by multiple NIH and NSF awards. Dr. Ruan has helped lead several major federally funded efforts at UT San Antonio and UT Health San Antonio to strengthen computational infrastructure, foster crossdisciplinary collaboration, and expand training and education in biomedical data science across South Texas. He directs an active research group that mentors students from diverse backgrounds in computational biology and biomedical data science, many of whom have continued on to impactful careers in academia, industry, and government.
SECTION 3: RESEARCH OVERVIEW  

Please provide a summary of your research focus/interests. 

SUMMARY 
Dr. Jianhua Ruan’s research is centered around bioinformatics, computational biology, and machine learning. He is particularly focused on developing machine learning methods for modeling cis-regulatory networks, analyzing complex biological networks using graph algorithms, and employing data mining techniques for biomarker discovery and disease prediction through integrated analysis of omics data. His work has been primarily funded by the National Institutes of Health (NIH) and the National Science Foundation (NSF). 

 

SECTION 4: PUBLICATIONS & PRESENTATIONS

Please provide a summary of your past, current, and planned publications and presentations.

SUMMARY 
Representative publications:

  1. Saimon, S. H., Najnin, T., & Ruan, J. (2024). A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2512-2523).
  2. Kusi, M., Zand, M., Lin, L.L., Chen, M., Lopez, A., Lin, C.L., Wang, C.M., Lucio, N.D., Kirma, N.B., Ruan, J. and Huang, T.H.M., (2022). 2-Hydroxyglutarate destabilizes chromatin regulatory landscape and lineage fidelity to promote cellular heterogeneity. Cell reports, 38(2).
  3. Adnan, N., Zand, M., Huang, T. H., & Ruan, J. (2021). Construction and evaluation of robust interpretation models for breast cancer metastasis prediction. IEEE/ACM transactions on computational biology and bioinformatics, 19(3), 1344-1353.
  4. Bi, M., et al. Enhancer reprogramming driven by high-order assemblies of transcription factors promotes phenotypic plasticity and breast cancer endocrine resistance. Nature cell biology 22.6 (2020): 701-715. 
  5. Gao, Z., & Ruan, J. (2017). Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning. Bioinformatics, 33(14), 2097-2105.
  6. Jahid, M. J., Huang, T. H., & Ruan, J. (2014). A personalized committee classification approach to improving prediction of breast cancer metastasis. Bioinformatics, 30(13), 1858-1866. 
  7. Lei, C., & Ruan, J. (2013). A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity. Bioinformatics, 29(3), 355-364. 
  8. Ruan, J., Dean, A. K., & Zhang, W. (2010). A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC systems biology, 4, 1-21. 

 

SECTION 5: EDUCATIONAL OUTREACH & ENGAGEMENT 

Please provide a summary of your educational outreach and engagement activities.

SUMMARY 
Dr. Ruan has played a key role in advancing research and training initiatives in computational biology and biomedical data science at the University of Texas at San Antonio (UTSA). As the Principal Investigator of the NIH-funded Computational Biology Initiative at UTSA, he has helped provide vital research support and training to the biomedical research community in South Texas. He has also served as a co-leader for the Informatics Cluster of the Research Centers in Minority Institutions Translational Research Network (RTRN), a co-Investigator on an NCI-funded P20 program aimed at training undergraduate and master’s students in cancer genomics, and a project co-leader and data core co-leader for the NCI-sponsored U54 Center for Cancer Systems Biology at San Antonio. In addition, through his research lab at UTSA, Dr. Ruan has offered biomedical data science training opportunities for numerous graduate and undergraduate students, including more than a dozen female and Hispanic students. Many of his mentees have published high-quality research papers under his guidance and have gone on to pursue biomedical data science research careers in academia or industry. 

 

SECTION 6: ADDITIONAL RESOURCES

Please provide any additional information here. 

SUMMARY 
Examples: Links to ResearchGate, Google scholar, ORCID, LinkedIn, GitHub, or Personal website Memberships in Scientific societies

https://scholar.google.com/citations?user=seB8s3wAAAAJ&hl=en&oi=ao