Joseph Daniel Romano, PhD, MPhil, MA

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Assistant Professor of Biostatistics and Epidemiology
CEET Investigator, Center of Excellence in Environmental Toxicology
Faculty member, Institute for Translational Medicine and Therapeutics
Senior Fellow, Institute for Biomedical Informatics, University of Pennsylvania
Chair, Community Affairs and Professional Development Committee; Department of Biostatistics, Epidemiology and Informatics
Department: Biostatistics and Epidemiology
Graduate Group Affiliations

Contact information
403 Blockley Hall
423 Guardian Drive
Philadelphia, PA 19104
Office: 215-573-5571
B.S. (Molecular Genetics)
University of Vermont, 2014.
M.A. (Biomedical Informatics)
Columbia University, 2016.
MPhil (Biomedical Informatics)
Columbia University, 2018.
PhD (Biomedical Informatics)
Columbia University, 2019.
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Description of Research Expertise

Joseph D. Romano is an Assistant Professor of Informatics and Pharmacology at the University of Pennsylvania. He earned a BS degree in Molecular Genetics from the University of Vermont, followed by MA, MPhil, and PhD degrees in Biomedical Informatics from Columbia University.

The Romano Lab conducts original research at the interface of clinical/translational informatics and environmental health, with particular focus on developing new artificial intelligence (AI) models that explain the biology underlying environmental risk factors and mechanisms of disease. Other areas of research interest include autoimmune-related adverse events, maternal environmental health, and natural products drug discovery. Dr. Romano leads the development of several major biomedical knowledge bases, including ComptoxAI, VenomKB, and the Alzheimer’s Knowledge Base.

Selected Publications

Albi G, Dagliati A, Vavassori C, Pisani L, Bellazzi R, Romano JD, & Colombo GI: Classification of Coronary Artery Disease Severity by Combining Clinical and Gene Expression Variables. 22nd International Conference on Artificial Intelligence in Medicine 22, July 2024.

Romano JD, Truong V, Kumar R, Venkatesan M, Graham BE, Hao Y, Matsumoto N, Li X, Wang Z, Ritchie M, Shen L, & Moore JH: The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research. Journal of Medical Internet Research 26: e46777, April 2024.

Li R, Romano JD, Chen Y, & Moore JH: Centralized and Federated Models for the Analysis of Clinical Data. Annual Review of Biomedical Data Science 7, 2024 Notes: (Accepted; in press).

Paris CF, Morales E, & Romano JD: Knowledge-driven artificial intelligence enables mechanistic computational toxicology. NIEHS Environmental Health Sciences Core Centers Annual Meeting October 2023.

Hao Y, Romano JD, & Moore JH: Knowledge Graph Aids Comprehensive Explanation of Drug and Chemical Toxicity. CPT: Pharmacometrics & Systems Pharmacology. Wiley, 12(8): 1072-1079, August 2023.

Romano JD, Li H, Napolitano T, Realubit R, Karan C, Holford M, & Tatonetti NP: Discovering venom-derived drug candidates using differential gene expression. Toxins. MDPI, 15(7): 451, July 2023.

Romano JD, Mei L, Senn J, Moore JH, & Mortensen HM: Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science. Computational Toxicology. Elsevier, 25: 100261, February 2023.

Romano JD: Omics Methods in Toxins Research—A Toolkit to Drive the Future of Scientific Inquiry. Toxins 14(11): 761, November 2022.

Manduchi E, Romano JD, & Moore JH: The promise of automated machine learning for the genetic analysis of complex traits. Human Genetics 141(9): 1529–1544, September 2022.

Hao Y, Romano JD, & Moore JH: Knowledge-guided deep learning models of drug toxicity improve interpretation. Patterns 3(9), August 2022.

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Last updated: 06/11/2024
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