Scientific publications
Peer-reviewed articles
TPOT-NN: Augmenting tree-based automated machine learning with neural network estimators
Genetic Programming and Evolvable Machines. (DOI: 10.1007/s10710-021-09401-z)
Ten Simple Rules for Writing a Paper About Scientific Software
PLoS Computational Biology. (DOI: 10.1371/journal.pcbi.1008390)
Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analysis
BMC Bioinformatics. (DOI: 10.1186/s12859-020-03755-4)
Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives
Front. Genet., 30 April 2019. (DOI: 10.3389/fgene.2019.00368)
A Decade of Translational Bioinformatics: A Retrospective Analysis of "Year-in-Review" Presentations
AMIA 2019 Informatics Summit.
Using a Novel Ontology to Inform the Discovery of Therapeutic Peptides from Animal Venoms
AMIA Summits on Translational Science Proceedings, 2019.
Systems Biology Approaches for Identifying Adverse Drug Reactions and Elucidating their Underlying Biological Mechanisms
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
Scientific Data, 2, 150065 (2015). (DOI: 10.1038/sdata.2015.65)
Adapting Simultaneous Analysis Phylogenomic Techniques to Study Complex Disease Gene Relationships
Journal of Biomedical Informatics, 54, 10-38 (2014). (DOI: 10.1016/j.jbi.2015.01.002)
Preprints
Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks
bioRxiv, 2021.08.08.455550 (2021). (DOI: 10.1101/2021.08.08.455550)
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
arXiv:2021.00058 (2018).
VenomKB v2.0: A knowledge repository for computational toxinology
bioRxiv, 295204 (2018). (DOI: 10.1101/295204)
Discovering therapeutic activities from venoms using differential gene expression
bioRxiv, 699280 (2019). (DOI: 10.1101/699280)