|Notification of Acceptance
National Center for Biotechnology Information
Computational Biology Branch
Teresa Przytycka is a Senior Principal Investigator at the National Center for Biotechnology Information in NLM. The research in her group focuses on computational methods advancing the understanding of biomolecular systems and the emergence of complex phenotypes, such as cancer. Her group also develops new computational approaches to study gene regulation including methods to reconstruct Gene Regulatory Networks, single cell analysis, network-based approaches to study mutational processes in cancer and drug response. In addition to genome-wide systems level analysis, her group develops algorithms that help to utilize HT-SELEX technology for the identification of RNA binding motifs and drug design.
In 2021 Przytycka was named a Fellow of the International Society for Computational Biology. She serves as an editor of serval computational biology journals including PloS Computational Biology, Bioinformatics, BMC Algorithms for Molecular Biology, among other journals. She is also a member of the steering committee of Research in Computational Molecular Biology (RECOMB) – one of the most prestigious algorithmic computational biology conferences bridging the areas of computational, mathematical, statistical, and biological sciences.
For more information, visit Dr. Przytycka's webpage
Title: Delineating relation between mutagenic signatures, cellular processes, and environment through computational approaches
Cancer genomes accumulate many somatic mutations resulting from carcinogenic exposures, cancer related aberrations of DNA maintenance machinery, and normal stochastic events. These processes often lead to distinctive patterns of mutations, called mutational signatures. However interpreting mutation patterns represented by such signatures is often challenging. This talk will focus on computational methods to elucidate the relations between mutational signatures and cellular and environmental processes developed in my group. In particular, I will discuss computational methods to untangle the contributions of DNA damage and repair processes to mutation signatures and network based approaches to uncover the interactions between mutational signatures and biological processes.
University of North Carolina at Chapel Hill
Gillings School of Global Public Health
Department of Epidemiology
Dr. Nora Franceschini is a nephrologist and an epidemiologist with a 20-year experience conducting multidisciplinary studies in chronic kidney disease and mentoring graduate students and early-stage investigators. Her research focuses on environmental and genetic determinants of hypertension, kidney and cardiovascular diseases in diverse and underrepresented populations who carry a high burden of chronic health conditions. This work includes gene discovery and multi-omic studies in American Indians, African Americans and Hispanics/Latinos, and studies to characterize the social/environmental context in which genetic variants contribute to health conditions. She is also leading research in chronic kidney disease in agricultural communities in Central America.
For more information, visit Dr. Franceschini's webpage
Title: Genomics and Human Population Diversity
Research in diverse populations is critical to uncover disease etiology and estimate disease risk. Populations vary on disease burden and their DNA make-up, including the frequencies of alleles at genetic variants. The extent to which disease-associated genetic risk is specific to a particular population or shared across populations may be related to differences in genetic variation. Admixed populations can carry genetic risk driven by population specific variants from less characterized populations. Our studies in diverse populations have leveraged differences in population genetic architecture to map loci associated with disease and biomarkers of disease. We have also shown that genome-wide association studies from diverse populations improve disease risk prediction of polygenic risk scores. Increasing genomics resources in diverse populations will be important to fully characterize disease risk in populations and to provide insights into possible disease mechanisms. Approaches that incorporate the complexities of genetic diversity should improve the utility of genomics in global populations for prevention and clinical care.
Division of Bioinformatics and Biostatistics
National Center for Toxicological Research (NCTR)
U.S. Food and Drug Administration
Dr. Weida Tong is Director of the Division of Bioinformatics and Biostatistics at FDA’s National Center for Toxicological Research (NCTR). He has been an FDA Senior Biomedical Research and Biomedical Product Assessment Service expert since 2011, an Arkansas Research Alliance fellow since 2016, and a member of the Arkansas Academy of Computing since 2021. He has served on science advisory boards for several multi-institutional projects in Europe and the U.S. and also holds adjunct appointments at several universities. His primary research interests are in the fields of bioinformatics, artificial intelligence (AI), molecular modeling and data analytics for biomarker discovery, drug safety and repurposing, pharmacogenomics/toxicogenomics, and precision medicine.
Dr. Tong has published over 300 peer-reviewed papers and book chapters.
For more information, visit Dr. Tong's webpage
Title: The Ascent of AI: Predicting Drug-Induced Liver Injury
Artificial intelligence (AI) has made a significant mark in the past decade and demonstrated its utility in the broad area of predictive toxicology and clinical application. The rapid advancement in AI also presents several opportunities and challenges to regulatory agencies with questions such as (1) how to assess and evaluate AI-based products and (2) how to develop and implement AI-based application to improve the agencies functions. In this presentation, the current thinking and on-going efforts at FDA in the area of regulatory science research will be discussed with a focus on drug safety. AI consists of two application categories, predictive and generative; both are critical to assess drug safety. Predictive algorithms learn from existing data/information to predict future outcomes, while generative algorithms produce new data with AI-driven study design. Examples will be given from the FDA projects in both AI application categories with assessing drug-induced liver injury (DILI). Specifically, a novel deep learning model for DILI prediction (called DeepDILI) will be introduced to support drug discovery and development through the FDA ISTAND qualification mechanism (ISTAND: Innovative Science and Technology Approaches for New Drugs). In addition, a generative model using GAN method called AnimalGAN will be discussed as an alternative way of replacing animal model with AI. The presentation will conclude with a general framework of combining both discriminatory and generative AI in drug safety assessment.