서울대학교 생물정보학 협동과정 주최로 세미나를 아래와 같이 열고자 하오니 많은 참여 바랍니다.

세미나는 Zoom을 통한 온라인 강의로 예정되어 있으며 강의에 참여하고자 하시는 분께서는 아래의 링크로 참여 부탁드리겠습니다.

일시: 2021년 11월 18일 목 요일 오전 11시

연사: 조영래 연사님 (연세대학교)

Zoom link:　https://snu-ac-kr.zoom.us/j/83095296500%26sa%3DD%26source%3Dcalendar%26ust%3D1637365489393725%26usg%3DAOvVaw3kWq6YtAKQfLxIE03Hcpgf&source=gmail&ust=1637020359428000&usg=AOvVaw0ameapCxn_UVS0sXK98Fn-" style="color: rgb(26, 115, 232); font-family: Roboto, Arial, sans-serif; font-size: 14px; letter-spacing: 0.20000000298023224px; white-space: pre-wrap;">https://snu-ac-kr.zoom.us/j/83095296500

Title:　Network-Based Computational Approaches in Bioinformatics and Biomedicine

ABSTRACT :

Recent studies of the relationships among biological or biomedical entities are shifting to a new paradigm that characterizes system-level analysis. In this scenario, a systematic study of biological or biomedical networks provides the basis for explicating complex mechanisms to perform cellular functions or cause diseases. As the most representative example of such networks, interactions between biological molecules on the scale of the entire genome, which collectively referred to as the interactome, are frequently used. In this study, we introduce a variety of network-based approaches for systematic analysis of genome-wide protein-protein interaction (PPI) networks and drug-target-disease association networks.　

PPI networks are a crucial resource in determining molecular organizations and biological processes. Interaction evidence not only indicates physical association but also strongly implies functional relatedness. Network-based computational approaches can predict the entire set of protein complexes as larger functional units. They can also identify signaling pathways illuminating cellular processes. The results provide significant clues to disease mechanisms because abnormal activation of signaling pathways may lead to diseases, such as cancer.　

Drug-target-disease association networks are underlying knowledge for drug repositioning, new uses for approved drugs. Network-based and machine learning approaches can predict drug-target interactions using drug-drug and protein-protein similarities. They can also be applied to disease gene prioritization. These methods handle diverse network features to improve prediction accuracy.