Joseph Daniel Romano, PhD, MPhil, MA

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Assistant Professor of Biostatistics and Epidemiology
CEET Investigator, Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania
Faculty member, Institute for Translational Medicine and Therapeutics
Senior Fellow, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
Department: Biostatistics and Epidemiology

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

My research focuses on using translational bioinformatics and artificial intelligence to discover links between chemical exposures and clinically significant human diseases. I am particularly interested in applications of these areas to environmental health science and computational toxicology, for the purpose of uncovering new mechanisms of chemical toxicity. This work involves knowledge base development, data harmonization, applied ontology design, and graph machine learning. I am also interested more broadly in clinical informatics, AI methods development, and informatics education.

Other areas in which I have research expertise include automated machine learning, ensemble learning, automated literature annotation, phylogenomics/phylogenetics, computational toxinology, drug discovery, biological sequence analysis, and data visualization.

Selected Publications

Romano, Joseph D.; Mei, Liang; Senn, Jonathan; Moore, Jason H.; Mortensen, Holly M.: Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science. Computational Toxicology. Elsevier, 2023 Notes: (in production).

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

Yun Hao, Joseph D. Romano, and Jason H. Moore: Knowledge Graph Aids Comprehensive Explanation of Drug Toxicity. bioRxiv(2022.10.07.511348), October 2022 Notes: bioRxiv preprint ahead of publication.

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, and Penning T: Automating toxicological knowledge discovery using ComptoxAI. Chemical Research in Toxicology 35(8): 1370-1382, July 2022.

Romano JD, Le TT, La Cava W, Gregg JT, Goldberg DJ, Chakraborty P, Ray NL, Himmelstein D, Fu W, Moore JH.: PMLB v1.0: An open-source dataset collection for benchmarking machine learning methods. Bioinformatics. Janet Kelso (eds.). 38(3): 878-80, February 2022.

Romano JD, Hao Y, Moore JH.: Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks. Pacific Symposium on Biocomputing 27: 187-198, January 2022 Notes: Conference proceedings represent peer reviewed research.

Joseph D. Romano, Trang T. Le, Weixuan Fu, & Jason H. Moore: TPOT-NN: Augmenting tree-based automated machine learning with neural network estimators. Genetic Programming and Evolvable Machines 22: 207-227, March 2021.

Romano JD, Moore JH.: Ten simple rules for writing a paper about scientific software. PLoS Computational Biology 16: e1008390, Nov 2020.

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Last updated: 01/23/2023
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