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Hatice ÜLkü Osmanbeyoğlu, PhD
- Associate Professor of Biomedical Informatics
- Associate Professor of Bioengineering
- Associate Professor of Computational and Systems Biology
Department of Biomedical Informatics (DBMI) | University of Pittsburgh School of Medicine
Biography
Dr. Hatice Ulku Osmanbeyoğlu is an Associate Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine. Her research focuses on developing computational and statistical frameworks to model gene regulatory programs and cellular interactions in cancer and complex diseases. She integrates multi-omics and spatial transcriptomics data to infer transcription factor activity, regulatory networks, and tissue microenvironment dynamics. Her work emphasizes mechanistic interpretation, reproducibility, and cross-cohort validation to enable robust biological discovery and translational insight.
Computational & Clinical Expertise
Dr. Osmanbeyoğlu’s expertise is in computational omics, systems biology, and statistical learning applied to clinically relevant biomedical research. She leads integrative analyses across cancer and disease contexts, with particular experience in transcriptomics, spatial transcriptomics, and multi-omics data integration. Her work focuses on quantifying gene regulatory activity and characterizing the tissue microenvironment using scalable and reproducible computational frameworks.
Research Interests
Dr. Osmanbeyoğlu’s research centers on developing integrative computational methods to infer gene regulatory programs from high-dimensional genomic and spatial data. She focuses on modeling transcription factor activity, pathway regulation, and cell–cell interactions within the tissue microenvironment across diverse disease contexts. Her work includes the design of scalable, reproducible pipelines for multi-omics integration and cross-cohort analysis. Ultimately, her research aims to translate complex molecular data into mechanistic insights that inform disease biology and therapeutic strategies.
Specialties
- Computational Omics
- Systems Biology
- Spatial Transcriptomics and Spatial Biology
- Transcriptional and Epigenetic Regulation
- Applied Machine Learning in Biomedicine
- Cancer Genomics and Tumor Microenvironment
Websites & Links
Education & Training
- B.S., Computer EngineeringNortheastern University, Boston, MA2002–2004
- M.S., Electrical and Computer EngineeringCarnegie Mellon University, Pittsburgh, PA2004–2005
- M.S., BioengineeringUniversity of Pittsburgh, Pittsburgh, PA2006–2009
- Ph.D., Biomedical InformaticsUniversity of Pittsburgh, Pittsburgh, PA2009–2012
- Postdoctoral FellowMemorial Sloan Kettering Cancer Center, New York, NY2013–2018
Awards & Distinctions
- Early Investigator Advancement Program (EIAP) ScholarNIH/NCI, 2022
- Hillman Early-Career Fellow for Innovation in Cancer ResearchUniversity of Pittsburgh, 2019
- Postdoctoral Research AwardMemorial Sloan Kettering, 2017
- K99/R00 Pathway to Independence AwardNIH/NCI, 2016
Representative Publications
Zhang L, Sagan A, Qin B, Wang H, Kim E, Hu B, Osmanbeyoglu HU. STAN, a computational framework for inferring spatially informed transcription factor activity. Nucleic Acids Research 2026;54. doi: 10.1093/nar/gkaf1473.
Zhang L, Cascio S, Mellors JW, Buckanovich RJ, Osmanbeyoglu HU. Single-cell analysis reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of ovarian cancer. Commun Biol. 2024;7(1):20. doi: 10.1038/s42003-023-05733-x. PMID: 38182756.
Ma X, Lembersky D, Kim ES, Becich MJ, Testa JR, Bruno TC, Osmanbeyoglu HU. Spatial landscape of malignant pleural and peritoneal mesothelioma tumor immune microenvironments. Cancer Res Commun. 2024;4(8):2133-2146. doi: 10.1158/2767-9764.CRC-23-0524.
Tao Y*, Ma X*, Palmer D, Schwartz R, Lu X, Osmanbeyoglu HU. Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers. Nucleic Acids Res. 2022;50(19):10869-10881. doi: 10.1093/nar/gkac881. PMID: 36243974.
Ma X, Somasundaram A, Qi Z, Hartman DJ, Singh H, Osmanbeyoglu HU. SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators. Nucleic Acids Res. 2021;49(17):9633-47. doi: 10.1093/nar/gkab745. PMID: 34500467.
Research Grants
| Grant / Funding Agency | Role | Grant Number | Years |
|---|---|---|---|
| Integrative framework for surface protein imputation for spatial biology of cancer NIH/NCI | PI | R21CA294196-01A1 | 2025–2027 |
| C-DETECT: Cancer Detection for Early Tumors using Enhanced Cell Targeting ARPA-H | Co-I | AWD00011885 | 2025–2030 |
| Computational methods for delineating cell context-specific regulatory programs NIH/NIGMS | PI | R35GM1412820 | 2022–2027 |
| The Role of EGFL6 in Ovarian Tumor Immunity NIH/NCI | Co-I | R01CA276279 | 2023–2028 |
| Evaluating unique aspects of quiescent ovarian cancer cell biology for therapeutic targets NIH/NCI | Co-I | R01CA276279 | 2023–2028 |
Trainees
| Trainee | Degree Program / Role & Years |
|---|---|
| Jiahui Hou | Post-Doctoral Scholar, Biomedical Informatics Training Program | 2024–Present |
| Jonathan Elliot Perdomo | Post-Doctoral Scholar, Biomedical Informatics Training Program | 2025–Present |
| Maryam Mirzamostafa | Post-Doctoral Scholar, Biomedical Informatics Training Program | 2025–Present |
| Saiful Islam | Post-Doctoral Scholar, Biomedical Informatics Training Program | 2025–Present |
| Haoyu Wang | PhD Trainee, Biomedical Informatics Training Program | 2025–Present |