William M. Southerland, PhD1, ClarLynda Williams-Devane, PhD2, Michael Campbell, PhD3, S. Joshua Swamidass, MD, PhD4, Greg Hampikian, PhD5, Janet Layne6
11Howard University College of Medicine
2Fisk University, Data Science and Bioinformatics
3Howard University, Department of Biology
4Washington University in Saint Louis
5Boise State University, Department of Biology, Co-Director of the Idaho Innocence Project
6Boise State University, Department of Biology
A common theme to all definitions of health disparities include differences in the health status and/or health outcomes among population groups. These groups may be identified by ethnicity, gender, sexual orientation, or age. Biocomputing has the potential to leverage existing data to unlock the complexities of understanding race and ethnicity, age, gender, and sexual orientation as conceptualizations and no longer fixed attributes leading to more realistic and accurate models of health disparities
Additionally, many significant health differences are not classified as health disparities. Documented health disparities are often associated with avoidable health differences among populations. Health disparities impact public health as a whole, while increasing overall healthcare costs. Disparities among population groups are prevalent in societies around the world, resulting in significant global costs. Disparities among population groups are prevalent in societies around the world, resulting in significant global costs. Nationally, these same disparities are also seen in the multiple entities from social and health policies to the justice system. Similarly, artificial intelligence and other biocomputing methods are being applied to better understand the complexities.Available data indicate that both biological factors (e.g. genomic and epigenetic variation) as well as social determinants of health (SDOH) are important drivers of health disparities. These SDOH include: Economic Stability, Education, Social and Community Context, Health and Health Care, Neighborhood and Built Environment. Biological factors and SDOH and can provide context to questions of societal justice..
The Pacific Symposium on Biocomputing (PSB) 2021 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological and biomedical significance. PSB 2021 brings together top researchers from around the world to exchange research results and address open issues in all aspects of computational biology.
The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within Biocomputing and provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field. As a result, PSB 2021 provides an ideal venue for the vibrant exchange of ideas and insights around issues related to the utilization of computational methods to better understand aspects of disparity among population groups
Interested researchers please see the call for abstracts submit a one-page abstract of your presentation to firstname.lastname@example.org by August 3, 2020. References and one figure are optional and do not contribute to page limit. Invitations for presentation will be sent out by August 17, 2020.
William Southerland, PhD. is a Professor of Biochemistry in the Howard University College of Medicine. He is also the Director of the Howard University Center for Computational Biology and Bioinformatics (CCBB) and the Principal Investigator of the Howard University Research Centers in Minority Institutions (RCMI) Program. His research interests include investigating the rules of molecular recognition and interactions using molecular dynamics simulation to correlate conformational changes with time-dependent changes in atom-atom contacts between interacting species and with any associated changes in time-dependent interaction energies. This approach is used to study interaction mechanisms of small molecules with both proteins and DNA and has important implications for design of therapeutic agents. Also, of interest is the utilization of molecular dynamics and time-dependent interaction energy calculations to decode the nucleotide sequence dependency on ligand recognition by DNA. His interests also include obesity-related SNP analysis in diverse populations and the development of tools for data mining of EHR data.
ClarLynda Williams-DeVane, PhD, is an assistant professor at North Carolina Central University. She is also the director of the Bioinformatics, Genomics, and Computational Chemistry Core (BGCCC) and leader of the Integrative Data Science Approaches to Health Disparities research team, where she leads the development of integrative methods and approaches in the secondary analysis of disparate complex disease data. She teaches interdisciplinary Biostatistics and Bioinformatics courses from the undergraduate to doctoral level. Dr. Williams-DeVane is currently focused on the disparities of maternal mediated childhood obesity (MMCO), childhood asthma, preterm birth, and breast cancer.
S. Joshua Swamidass, MD, PhD. a physician and a scientist, is an Associate Professor of Laboratory and Genomic Medicine at Washington University in Saint Louis ( http://swami.wustl.edu/ ). He is the Faculty Lead for Translational Bioinformatics at the university’s Institute for Informatics, and his group is funded by the NIH to build Deep Learning models of drug toxicity in adults and children.
Greg Hampikian, PhD, is Professor of Biology, and Criminal Justice at Boise State University (BSU). He is also the Co-Director of the Idaho Innocence Project at BSU. He has been the DNA expert on more than 20 exonerations in the US and overseas. Hampikian’s New York Times Opeds, include "The Dangers of DNA Testing" (2018), "When May I Shoot a Student?" (2014), and “Men Who Needs Them?" (2012). His book Exit to Freedom with exoneree Calvin Johnson, chronicles Mr. Jonson’s 17-year fight to prove his innocence using DNA, in a case laden with racial bias. Hampikian's research includes Subjectivity and Bias in Complex DNA Analysis, and discovery of the smallest DNA and protein sequences absent from nature that he has termed Nullomers.
Janet Layne, Ph.D. candidate, is a molecular biologist with seven years industry experience in genetically modified organisms at the lab bench, in bioinformatics and with international regulatory agencies. Layne has also published her work on the utility of nanoparticles in cancer treatment. She is currently working as a DNA analyst with the Idaho Innocence Project at Boise State University, while she pursues a Ph.D. in computer science at Boise State University.
Michael Campbell, PhD, is an Assistant Professor in the Department of Biology at Howard University where his research activities focus on three main areas of interest: A) Evolutionary and systematic biology, B) Genomic basis of complex disease and normal variable traits, and C) Computational methods development. Dr. Campbell first obtained his Bachelor of Science degree in Physical Anthropology at the University of Toronto and then earned a Master of Science degree in Human Biology at Oxford University. Dr. Campbell went on to obtain his PhD in Human Evolutionary Genetics at Columbia University in New York City and completed postdoctoral training in the Department of Genetics at the University of Pennsylvania where he studied the evolution of genetic and phenotypic variation in diverse African populations. Dr. Campbell then applied his knowledge of African populations to study the genetic basis of ethnic disparities in cancer susceptibility in African Americans as research faculty in the Department of Biostatistics in the School of Public Health at Yale University.