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Joseph Daniel Romano, PhD, MPhil, MA

Assistant Professor of Biostatistics and Epidemiology
Department: Biostatistics and Epidemiology

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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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, 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.

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: 02/01/2023
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