Research > Research Areas

Translational Bioinformatics and Omics Research

Translational bioinformatics and omics research uncovers molecular mechanisms of disease from high-dimensional genomic and proteomic data. This work spans protein-protein interaction prediction, gene regulatory program inference, spatial biology, single-cell RNA-seq analysis and network-based drug repurposing.

We have a long-standing history of applying machine learning and computational methods to uncover molecular mechanisms of disease, including biomarker discovery, protein interaction network modeling and systems biology. This work has translated high-dimensional molecular data into biologically interpretable and clinically actionable knowledge, supported by sustained federal funding from the National Institutes of Health and National Cancer Institute.

Projects

Omics-driven biomarker discovery

This work focuses on biomarker discovery and predictive modeling from large, complex biomedical datasets spanning genomic, proteomic, metabolomic and microbiome data. Using integrative modeling and hybrid machine learning methods, the research applies rule learning techniques to biomarker discovery across a range of diseases, including lung, breast and esophageal cancers, as well as neurodegenerative and cardiovascular conditions. Biomarkers for early detection of lung and esophageal cancers have been validated across institutional studies, including through a decade of coleadership of the Bioinformatics and Biostatistics Core of the National Cancer Institute-funded Lung SPORE (Specialized Programs of Research Excellence) project.

Faculty: Vanathi Gopalakrishnan, PhD

Collaborators: William L. Bigbee, PhD, and Jill M. Siegfried, PhD

Sample Publication: Bigbee WL, Gopalakrishnan V, Weissfeld JL, Wilson DO, Dacic S, Lokshin AE, Siegfried JM. A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-risk individuals found to be cancer-free by CT screening. J Thorac Oncol. 2012 Apr;7(4):698-708.

The Female Digital Health Twin Global Alliance (FDHT-GA)

The Female Digital Health Twin Global Alliance (FDHT-GA) aims to revolutionize precision medicine by building a globally inclusive, artificial intelligence (AI)-powered framework that addresses the historical underrepresentation of women in clinical research. The FDHT-GA project lays the infrastructure for the first comprehensive female digital health twin, leveraging advanced machine learning to model complex women's health trajectories across the lifespan, enabling disease prediction and the simulation of personalized treatments. By moving beyond one-size-fits-all medicine, the initiative addresses critical sex-specific gaps in research and uncovers how biological sex and diverse social determinants shape health outcomes. For faculty and students in biomedical informatics, the alliance offers a unique opportunity to integrate multiomic data and predictive analytics into a trustworthy, interoperable system, advancing AI innovation that is both equitable and lifesaving for women worldwide.

Faculty: Vanathi Gopalakrishnan, PhD

Collaborators: Srinivasan Suresh, MD, MBA, FAAP, Yanshan Wang, PhD, Muge Finkel, PhD, Rebecca C. Thurston, PhD, and Eldin Jasarevic, PhD

Protein interaction networks and disease mechanisms

This work applies machine learning and artificial intelligence to discover molecular mechanisms of disease, computationally uncovering thousands of previously unknown molecular interactions that shed light on disease development. The primary focus is on systems biology, specifically protein-protein interaction prediction at the system level, with outcomes applied to translational bioinformatics. These interaction networks are then used to address translational questions, including identifying potentially repurposable drugs for conditions such as schizophrenia and generating testable hypotheses for disease pathogenesis by integrating genomic, epidemiological and protein interaction data.

Faculty: Madhavi Ganapathiraju, PhD

Collaborators: Robert A. Sweet, MD, and Vishwajit L. Nimgaonkar, MD, PhD

Sample Publication Ganapathiraju MK, Thahir M, Handen A, Sarkar SN, Sweet RA, Nimgaonkar VL, Loscher CE, Bauer EM, Chaparala S. Schizophrenia interactome with 504 novel protein-protein interactions. NPJ Schizophr. 2016 Apr 27;2:16012.

Signaling pathways and computational systems biology

This work focuses on computational methods for identifying signaling pathways underlying biological processes and diseases, as well as statistical methods for acquiring knowledge from biomedical literature. Drawing on a background in pharmacology and biomedical informatics, the research applies latent variable models to simulate biological signaling systems and integrates text mining to extract and conceptualize molecular findings from scientific literature. These approaches are applied to translational questions in cancer and other diseases, including the identification of common disease mechanisms shared by tumors of different tissue origins through semantic representations of genomic alterations and topic modeling. Together, this body of work bridges computational systems biology and precision medicine by transforming complex molecular data into interpretable, clinically relevant knowledge.

Faculty: Xinghua Lu, MD, PhD

Collaborator: John Paisley, PhD

Sample Publication Chen V, Paisley J, Lu X. Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling. BMC Genomics. 2017 Mar 14;18(Suppl 2):105.

HIV mutation analysis in prophylactic clinical trials in Africa

The Genomics Analysis Core developed bioinformatics pipelines to detect low-frequency mutations in key HIV genes, including reverse transcriptase and integrase, associated with resistance to HIV prophylaxis in clinical trials conducted in Africa.

Faculty: Uma Chandran, MPH, PhD, MSIS

Collaborators: Urvi Parikh, PhD, and John Melors, MD

Sample Publication Parikh UM, Penrose KJ, Heaps AL, Halvas EK, Goetz BJ, Gordon KC, Hardesty R, Sethi R, Schwarzmann W, Szydlo DW, Husnik MJ, Chandran U, Palanee-Phillips T, Baeten JM, Mellors JW; MTN-020 Study Team. HIV-1 drug resistance among individuals who seroconverted in the ASPIRE dapivirine ring trial. J Int AIDS Soc. 2021 Nov;24(11):e25833.

Role of glucocorticoids in neural stem cells

The Genomics Analysis Core supported this multi-omics study examining how glucocorticoids influence neural stem cells. RNA-seq, ATAC-seq, and ChIP-seq data were integrated to characterize chromatin conformation, promoter occupancy and downstream transcriptional effects. The work has translational relevance for understanding neural reprogramming in neonates treated with glucocorticoids.

Faculty: Uma Chandran, MPH, PhD, MSIS

Collaborators: Don Defranco, PhD, and Fangping Mu, PhD

Sample Publication: Berry KJ, Chandran U, Mu F, Deochand DK, Lei T, Pagin M, Nicolis SK, Monaghan-Nichols AP, Rogatsky I, DeFranco DB. Genomic glucocorticoid action in embryonic mouse neural stem cells. Mol Cell Endocrinol. 2023 Mar 1;563:111864.