Lab Publications (newest first)

The following publications were either written by members of the lab, or were contributed to significantly by a lab member as a coauthor.

Discovering Venom-Derived Drug Candidates Using Differential Gene Expression

Romano JD, Li H, Napolitano T, Realubit R, Karan C, Holford M, and Tatonetti NP.

Toxins. 2023;15(7):451. (DOI: 10.3390/toxins15070451)

Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science

Romano JD, Mei L, Senn J, Moore JH, and Mortensen HM.

Computational Toxicology. 2023;25:100261. (DOI: 10.1016/j.comtox.2023.100261)

Omics Methods in Toxins Research - A toolkit to Drive the Future of Scientific Inquiry

Romano JD.

Toxins. 2022;14(11):761. (DOI: 10.3390/toxins14110761)

Knowledge-guided deep learning models of drug toxicity improve interpretation

Hao Y, Romano JD, and Moore JH.

Patterns. 2022 Aug 24;3(9):100565. (DOI: 10.1016/j.patter.2022.100565)

Automating predictive toxicology using ComptoxAI

Romano JD, Hao Y, Moore JH, and Penning TM.

Chem. Res. Toxicol. 2022;35(8):1370-1382. (DOI: 10.1021/acs.chemrestox.2c00074)

PMLB v1.0: An open source dataset collection for benchmarking machine learning methods

Romano JD, Le TT, La Cava W, Gregg JT, Goldberg DJ, Chakraborty P, Ray NL, Himmelstein D, Fu W, and Moore JH.

Bioinformatics. 2022 Feb 1;38(3):878-880. (DOI: 10.1093/bioinformatics/btab727)

Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks

Romano JD, Hao Y, and Moore JH.

Pac Symp Biocomput. 2022;27:187-198. (DOI: 10.1142/9789811250477_0018)

The promise of automated machine learning for the genetic analysis of complex traits

Manduchi E, Romano JD, and Moore JH.

Human Genetics. 2022;141:1529-1544. (DOI: 10.1007/s00439-021-02393-x)

TPOT-NN: Augmenting tree-based automated machine learning with neural network estimators

Romano JD, Le TT, Fu W, and Moore JH.

Genetic Programming and Evolvable Machines. 2021;22:207-227. (DOI: 10.1007/s10710-021-09401-z)

Ten Simple Rules for Writing a Paper About Scientific Software

Romano JD and Moore JH.

PLoS Comput Biol. 2020 Nov 12;16(11):e1008390. (DOI: 10.1371/journal.pcbi.1008390)

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analysis

Manduchi E, Fu W, Romano JD, Ruberto S, and Moore JH.

BMC Bioinformatics. 2020;21:430. (DOI: 10.1186/s12859-020-03755-4)

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

Romano JD and Tatonetti NP.

Front. Genet., 30 April 2019. (DOI: 10.3389/fgene.2019.00368)

A Decade of Translational Bioinformatics: A Retrospective Analysis of "Year-in-Review" Presentations

Romano JD, Bernauer M, McGrath S, Nagar SD, and Freimuth R.

AMIA 2019 Informatics Summit.

Using a Novel Ontology to Inform the Discovery of Therapeutic Peptides from Animal Venoms

Romano JD and Tatonetti NP.

AMIA Summits on Translational Science Proceedings, 2019.

Systems Biology Approaches for Identifying Adverse Drug Reactions and Elucidating their Underlying Biological Mechanisms

Boland MR, Jacunski A, Lorberbaum T, Romano JD, Moskovitch R, and Tatonetti NP.

WIREs Systems Biology and Medicine, 8(2), 104-122 (2016). (DOI: 10.1002/wsbm.1323)

VenomKB, A new knowledge base for facilitating the validation of putative venom therapies

Romano JD and Tatonetti NP.

Scientific Data, 2, 150065 (2015). (DOI: 10.1038/sdata.2015.65)

Adapting Simultaneous Analysis Phylogenomic Techniques to Study Complex Disease Gene Relationships

Romano JD, Tharp WG, and Sarkar IN.

Journal of Biomedical Informatics, 54, 10-38 (2014). (DOI: 10.1016/j.jbi.2015.01.002)