As huge amounts of global data (genomic, epigenomic, transcriptomic, proteomic, and metabolomic) generated from a broad spectrum of specimens collected from human patients have been accumulated in public repositories, together with electronic health records and drug treatment information, biology is now becoming an informational science. Accordingly, there have been significant needs for bioinformatic methods that can effectively extract useful information from these data. However, such information extraction has been hampered by the intrinsic characteristics of biological data: 1) complex entanglement of information related to causes and effects via feedback loops; and 2) prevalent of frequent information irrelevant to given questions. In this talk, I will present several representative studies in which various bioinformatics and systems approaches were developed for prioritization of causative information and identification of relevant information from omics data from complex diseases.

ZOOM(Online) : https://snu-ac-kr.zoom.us/j/83481895834