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Joseph Daniel Romano, PhD, MPhil, MA, FAMIA
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Assistant Professor of Biostatistics and Epidemiology
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Department: Biostatistics and Epidemiology
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Contact information
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3600 Civic Center Blvd
2b 5E 300-A
Philadelphia, PA 19104
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2b 5E 300-A
Philadelphia, PA 19104
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Office: 215-573-5571
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Publications
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Education:
21 9 B.S. 1f (Molecular Genetics) c
2e University of Vermont, 2014.
21 9 M.A. 23 (Biomedical Informatics) c
2c Columbia University, 2016.
21 a MPhil 23 (Biomedical Informatics) c
2c Columbia University, 2018.
21 8 PhD 23 (Biomedical Informatics) c
2c Columbia University, 2019.
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21 9 B.S. 1f (Molecular Genetics) c
2e University of Vermont, 2014.
21 9 M.A. 23 (Biomedical Informatics) c
2c Columbia University, 2016.
21 a MPhil 23 (Biomedical Informatics) c
2c Columbia University, 2018.
21 8 PhD 23 (Biomedical Informatics) c
2c Columbia University, 2019.
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Links
b7 Search PubMed for articles
70 The Romano Lab at the University of Pennsylvania
5c Joseph D. Romano - Personal Website
94 Joseph D. Romano - Google Scholar profile
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Permanent linkb7 Search PubMed for articles
70 The Romano Lab at the University of Pennsylvania
5c Joseph D. Romano - Personal Website
94 Joseph D. Romano - Google Scholar profile
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249 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.
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Description of Research Expertise
126 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.8
249 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.
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13a Abdurezak N, Pan IT, Sacksith K, Apostolidis S, & Romano JD: Defining an Electronic Health Record-Based Phenotyping Algorithm for Immune-Related Adverse Events. 2026 Penn Colton Center for Autoimmunity Symposium, Philadelphia, PA April 2026.
121 Kumar R, Romano JD, & Ritchie MD: Learnable Protein Representations in Computational Biology for Predicting Drug-Target Affinity. Journal of Cheminformatics 18(1): 25, Jan 2026 Notes: doi: 10.1186/s13321-025-01145-7.
216 Rajagopalan A, Nguyen TA, Guare LA, Garao Rico AL, Venkatesh R, Caruth L, Regeneron Genetics Center, Penn Medicine BioBank, Verma A, Ritchie MD, Hall MA, *Setia-Verma S, *Romano JD: DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs. 2026 Pacific Symposium on Biocomputing 31: 830-848, 2026 Notes: doi: 10.1142/9789819824755_0060; *co-corresponding/co-senior authors on publication.
10c Nguyen TA, Su W, Rajagopalan A, Abdurezak N, Hewryk OSI, & Romano JD: Clinical Knowledge Representation in Data Science. Annual Review of Biomedical Data Science 2026 Notes: (accepted; in-press).
e3 Hewryk OSI & Romano JD: A Phyto-Ontology for Integrative Therapeutic Discovery. Lecture Notes in Artificial Intelligence 2026 Notes: (accepted, in-press).
135 Andemariam B, Torres K, & Romano JD: Explainable AI-based prediction of chronic kidney disease as a long-term outcome of sickle cell disease in a large, multi-site observational data cohort. Blood 146(Supplement 1): 2967, November 2025.
f9 Kumar R, Romano JD, & Ritchie MD: CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity. Briefings in Bioinformatics 26(5): bbaf554, September 2025.
ef Kumar R, Romano JD, & Ritchie MD: Network-based analyses of multiomics data in biomedicine. BioData Mining 18(1): 37, May 2025 Notes: doi: 10.1186/s13040-025-00452-x.
ed Romano JD: Ista: An Ontology-Driven Toolkit for Biomedical Knowledge Base Assembly. Studies in Health Technology and Informatics May 2025 Notes: Accepted; in-press.
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Selected Publications
10c Aldeia GSI, Romano JD, de Franca FO, Herman DS, & La Cava WG: Towards symbolic regression for interpretable clinical decision scores. Philosophical Transactions A 384(2317): 20240588, April 2026.13a Abdurezak N, Pan IT, Sacksith K, Apostolidis S, & Romano JD: Defining an Electronic Health Record-Based Phenotyping Algorithm for Immune-Related Adverse Events. 2026 Penn Colton Center for Autoimmunity Symposium, Philadelphia, PA April 2026.
121 Kumar R, Romano JD, & Ritchie MD: Learnable Protein Representations in Computational Biology for Predicting Drug-Target Affinity. Journal of Cheminformatics 18(1): 25, Jan 2026 Notes: doi: 10.1186/s13321-025-01145-7.
216 Rajagopalan A, Nguyen TA, Guare LA, Garao Rico AL, Venkatesh R, Caruth L, Regeneron Genetics Center, Penn Medicine BioBank, Verma A, Ritchie MD, Hall MA, *Setia-Verma S, *Romano JD: DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs. 2026 Pacific Symposium on Biocomputing 31: 830-848, 2026 Notes: doi: 10.1142/9789819824755_0060; *co-corresponding/co-senior authors on publication.
10c Nguyen TA, Su W, Rajagopalan A, Abdurezak N, Hewryk OSI, & Romano JD: Clinical Knowledge Representation in Data Science. Annual Review of Biomedical Data Science 2026 Notes: (accepted; in-press).
e3 Hewryk OSI & Romano JD: A Phyto-Ontology for Integrative Therapeutic Discovery. Lecture Notes in Artificial Intelligence 2026 Notes: (accepted, in-press).
135 Andemariam B, Torres K, & Romano JD: Explainable AI-based prediction of chronic kidney disease as a long-term outcome of sickle cell disease in a large, multi-site observational data cohort. Blood 146(Supplement 1): 2967, November 2025.
f9 Kumar R, Romano JD, & Ritchie MD: CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity. Briefings in Bioinformatics 26(5): bbaf554, September 2025.
ef Kumar R, Romano JD, & Ritchie MD: Network-based analyses of multiomics data in biomedicine. BioData Mining 18(1): 37, May 2025 Notes: doi: 10.1186/s13040-025-00452-x.
ed Romano JD: Ista: An Ontology-Driven Toolkit for Biomedical Knowledge Base Assembly. Studies in Health Technology and Informatics May 2025 Notes: Accepted; in-press.
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