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
Department: Biostatistics and Epidemiology
Graduate Group Affiliations

Contact information
403 Blockley Hall
423 Guardian Drive
Philadelphia, PA 19104
Office: 215-573-5571
Education:
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 in computational toxicology and translational bioinformatics, with a focus on applying artificial intelligence to predict and explain the clinical outcomes of human exposure to toxic environmental chemicals. Dr. Romano leads the development of several major biomedical knowledge bases, including ComptoxAI, VenomKB, and the Alzheimer’s Knowledge Base. He is a member of the NIH/National Institute of Environmental Health Science's Environmental Health Language Collaborative (EHLC) - a major effort to standardize concept representation and reproducibility in environmental health research.

Selected Publications

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

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 therapeutic discovery in Alzheimer's Disease research. Journal of Medical Internet Research 2024 Notes: (Article 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.

Romano JD, Hao YH, Moore JH, & Penning T: Automating toxicological knowledge discovery using ComptoxAI. Chemical Research in Toxicology 35(8): 1370-1382, July 2022.

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Last updated: 02/21/2024
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