Abstract: Chemogenomics is an interdisciplinary research that utilizes chemicals/drugs and associated genomics data to systematically identify and analyze chemicals-protein/protein interactions for the purpose of enhancing new drug discovery. Computational chemogenomics draws from the cheminformatics/ bioinformatics and computational biology disciplines to produce useful information systems for researchers in pursuit of chemogenomics data-mining, predictive modeling, as well as techniques in ligand- and structure-based drug design.
Abstract: The concept of a Learning Health System, a system that can continuously and routinely study and improve itself, has gained significant traction around the country and around the world. Reports from the Institute of Medicine, many journal articles, and current federal policies argue strongly for achievement of this "Big Hairy Audacious Goal" to improve individual and population health. This presentation will describe the Learning Health System concept, framing it as a consummate informatics challenge that invokes in equal parts people and technolo
Abstract: Cellular signal transduction systems are organized as hierarchical network. When stimulated by environment changes, cellular signals are transmitted through signaling cascades in which signals are compositionally encoded. For example, the signal of an activated growth factor receptor, EGFR, is then compositional encoded by RAS, PI3K, AKT, and then by STAT3 and cJUN etc. Often, the effect of perturbation of cellular signaling system can be read out as changed gene expression.
Abstract: This talk will provide an overview of the new Center for Causal Discovery (CCD), which recently was funded as an NIH Big-Data-to-Knowledge (BD2K) Center of Excellence. The CCD is focused on developing and disseminating computational methods for causal modeling and discovery of biomedical knowledge from big data. Its aims include research, training, software dissemination, and collaborative projects with other BD2K Centers of Excellence.
Abstract: The nature of diagnostic healthcare is changing dramatically, thanks in part to the development of cost effective technologies for whole slide digital scanning, and high-throughput genetic, genomic and epigenetic data collection. There is a consensus among clinicians and researchers that integrative, co-analysis of clinical, genomic, histopathological and radiological data will open new and important venues for personalized medicine strategies in the near future.