Senior Computational Scientist — Division of Bioinformatics and Biostatistics
Roger Perkins received B.S. and M.S. degrees in nuclear engineering science in 1972 and 1974, respectively, from the University of Florida. He was a reactor physicist at General Atomics (GA) from 1974 to 1980, involved in research and development for the Gas Cooled Fast Breeder Reactor and various Department of Defense projects. From 1981 to 1984 he was the chief nuclear engineer at INESCO, a private company doing research and development on the Riggatron Compact Tokamak Fusion Reactor. He rejoined GA in 1984 where he was a program manager and nuclear codes specialist at the National Science Foundation’s new San Diego Supercomputing Center at the University of California, San Diego. He was the director of the U.S. Navy’s Advanced Scientific and Engineering Supercomputer Center from 1990 to1993.
From 1994 to 2008 Mr. Perkins managed NCTR’s scientific computing contract that encompassed IT systems, software development, biostatistics, and bioinformatics. He became an NCTR staff member in 2008, where he served as senior advisor in the Division of Biostatistics and Bioinformatics until 2018 when he assumed the position of Chief of the Biostatistics Branch. Mr. Perkins’ entire career has involved computational science — spanning physics, engineering, chemistry, and the biological sciences. He has been instrumental in promoting and advancing bioinformatics during his more than 24-year tenure at NCTR. He has served on several FDA governance boards and committees involving IT, scientific computing, and high-performance computing. He has been an FDA Scientific Computing Board member since its inception in 2007.
Mr. Perkins has served in various capacities, both administrative and research, since becoming a staff member in 2008. As both contractor manager and staff member at NCTR since 1994, he continually advocated for and fostered computational-science approaches in FDA research and regulatory sciences. While he was the scientific computing contract’s manager, he recruited some of the earliest bioinformaticists at NCTR, some of whom remain as senior scientists. Early research that preceded high-throughput molecular technologies was developing models to predict toxicity and biological activity based solely on chemical structure. Structure-activity relationship and chemometric-machine learning models were developed to predict which untested chemicals would exhibit endocrine activity mediated by nuclear receptors. Training sets were designed for diverse chemicals, and assays for those chemical’s binding to hormone receptors were run at NCTR; that data along with data from the literature formed NCTR’s first knowledge base anchored to endocrine-active chemicals.
The advent of microarrays and even more advanced sequencing provided opportunities for advancing research — and eventually precision clinical medicine — but not without computational challenges. Mr. Perkins participated in projects, including large international collaborations led by NCTR that focused on validating methods for discovering reliable biomarkers and predictive models from the extremely high-dimension molecular data, where false discovery risks are exceedingly high. Most recently, he has been involved with research projects employing Bayesian latent-variable methods, such as topic modeling for data modeling, labeling and information retrieval (including textual content), as well as developing text-mining software for regulatory-environment deployment.
Professional Societies/National and International Groups
Arkansas Bioinformatics Consortium's Steering Committee
2015 – Present
MidSouth Computational Biology and Bioinformatics Society
2013 – 2016
Publication titles are linked to text abstracts on PubMed.
A Comprehensive Assessment of RNA-Seq Accuracy, Reproducibility and Information Content by the Sequencing Quality Control Consortium.
Nature Biotechnology. 2014 Sep, 32: 903-914.
Comparing Next-Generation Sequencing and Microarray Technologies in a Toxicological Study of the Effects of Aristolochic Acid on Rat Kidneys.
Su Z., Li Z., Chen T., Li Q.Z., Fang H., Ding D., Ge W., Ning B., and Hong H.
Chemical Research in Toxicology. 2011 Sep 19, 24 (9): 1486-1493.
Next-Generation Sequencing and Its Applications in Molecular Diagnostics.
Su Z., Ning B., Fang H., Hong H., Perkins R., Tong W., and Shi L.
Expert Review of Molecular Diagnostics. 2011 Apr, 11 (3): 333-343.
The Microarray Quality Control (MAQC)-II Study of Common Practices for the Development and Validation of Microarray-Based Predictive Models.
Nature Biotechnology. 2010 Aug, 28 (8): 827-838.
The Balance of Reproducibility, Sensitivity, and Specificity of Lists of Differentially Expressed Genes in Microarray Studies.
Shi L., Jones W.D., Jensen R.V., Harris S.C., Perkins R.G., and Goodsaid F.M.
BMC Bioinformatics. 2008 Aug 12, 9 (Suppl 9): S10.
Reproducible and Reliable Microarray Results Through Quality Control: Good Laboratory Proficiency and Appropriate Data Analysis Practices are Essential.
Shi L., Perkins R.G., Fang H., and Tong W.
Current Opinion in Biotechnology. 2008 Feb, 19 (1): 10-18.
The Microarray Quality Control (MAQC) Project Shows Inter-and Intraplatform Reproducibility of Gene Expression Measurements.
Shi L., Reid L.H., Jones W.D., Shippy R., Warrington J.A., Baker S.C., and Collins P.J.
Nature Biotechnology. 2006 Sep, 24 (9): 1151-1161.
Cross-Platform Comparability of Microarray Technology: Intra-Platform Consistency and Appropriate Data Analysis Procedures are Essential.
Shi L., Tong W., Fang H., Scherf U., Han J., Puri R.K., Frueh F.W., and Goodsaid F.M..
BMC Bioinformatics. 2005 Jul, 6 (2): 1.
Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships.
Netzeva T.I., Worth A.P., Aldenberg T., Benigni R., Cronin M.T.D., and Gramatica P.
ATLA. 2005 Apr, 33: 155-173.
Arraytrack--Supporting Toxicogenomic Research at the US Food and Drug Administration National Center for Toxicological Research.
Tong W., Cao X., Harris S., Sun H., Fang H., Fuscoe J., Harris A., and Hong H.
Environmental Health Perspectives. 2003 Nov, 111 (15): 1819.
Quantitative Structure‐Activity Relationship Methods: Perspectives on Drug Discovery and Toxicology.
Perkins R., Fang H., Tong W., and Welsh W.J..
Environmental Toxicology and Chemistry. 2003 Aug, 22 (8): 1666-1679.
Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models.
Tong W., Hong H., Fang H., Xie Q., and Perkins R.
Journal of Chemical Information and Computer Sciences. 2003 Mar-Apr, 43 (2): 525-531.
Phytoestrogens and Mycoestrogens Bind to the Rat Uterine Estrogen Receptor.
Branham W.S., Dial S.L., Moland C.L., Hass B.S., Blair R.M., Fang H., and Shi L.
The Journal of Nutrition. 2002 Apr, 132 (4): 658-664.
Prediction of Estrogen Receptor Binding for 58,000 Chemicals Using an Integrated System of a Tree-Based Model with Structural Alerts.
Hong H., Tong W., Fang H., Shi L., Xie Q, Wu J., Perkins R., and Walker J.D.
Environmental Health Perspectives. 2002 Jan, 110 (1): 29.
Structure-Activity Relationships for a Large Diverse Set of Natural, Synthetic, and Environmental Estrogens.
Fang H., Tong W., Shi L.M., Blair R., Perkins R., Branham W., Hass B.S., and Xie Q.
Chemical Research in Toxicology. 2001 Mar, 14 (3): 280-294.
QSAR Models Using a Large Diverse Set of Estrogens.
Shi L.M., Fang H., Tong W., Wu J., Perkins R., Blair R.M., and Branham W.S.
Journal of Chemical Information and Computer Sciences. 2001 Jan-Feb, 41 (1): 186-195.
Quantitative Comparisons of In Vitro Assays for Estrogenic Activities.
Fang H., Tong W., Perkins R., Soto A.M., Prechtl N.V., and Sheehan D.M.
Environmental Health Perspectives. 2000 Aug, 108 (8): 723.
The Estrogen Receptor Relative Binding Affinities of 188 Natural and Xenochemicals: Structural Diversity of Ligands.
Blair R.M., Fang H., Branham W.S, Hass B.S., Dial S.L., Moland C.L., and Tong W.
Toxicological Sciences. 2000 Mar, 54 (1): 138-153.
Evaluation of Quantitative Structure-Activity Relationship Methods for Large-Scale Prediction of Chemicals Binding to the Estrogen Receptor.
Tong W., Lowis D.R. , Perkins R., Chen Y., Welsh W.J., and Goddette D.W.
Journal of Chemical Information and Computer Sciences. 1998 Jul-Aug, 38 (4): 669-677.
QSAR Models for Binding of Estrogenic Compounds to Estrogen Receptor Α and Β Subtypes.
Tong W., Perkins R., Xing L.I., Welsh W.J., and Sheehan D.M.
Endocrinology. 1997 Sep, 138 (9): 4022-4025.
- Contact Information
- Roger Perkins
- (870) 543-7391