[서울대 AI 여름학교] 신약개발과 맞춤의학을 위한 그래프 러닝 (김선 교수) скачать в хорошем качестве

[서울대 AI 여름학교] 신약개발과 맞춤의학을 위한 그래프 러닝 (김선 교수)

강연 제목: Graph Learning for Drug Discovery and Personalized Medicine 강연자: 서울대 컴퓨터공학부 김선 교수    • 김선교수님   서울대 AI 연구원은 2021년 8월 제2회 'AI 여름학교'를 개최하여 국내외 저명한 AI 연구자들의 강연을 모든 사람들에게 온라인으로 제공하였습니다. 3일간 1만명이 등교했던 여름학교 강의를 만나보시기 바랍니다. Abstract: "Many research problems in drug discovery and personalized medicine can be naturally formulated as graphs. In this formulation, each drug or patient is represented as a graph and classifying drugs or patients requires graph-level learning unlike node-level or link-level learning in many graph learning problems. In this talk, I will discuss some of recent works, a graph learning problem for toxicity prediction of small molecule drugs and another graph learning problem for cancer subtype and metastasis prediction using gene expression data, a.k.a, transcriptome. The first problem of toxicity prediction for drugs is a graph mining problem in search of sets of subgraph structures or toxicophores that can represent toxicity of drugs as graphs. Since we do not know toxicophores, a naive approach is to enumerate all possible subgraphs that are frequent in toxic drugs. However, this approach is computationally infeasible. We use Markovian random walks on chemical graphs to generate subgraphs and these subgraphs are screened for over-representation in toxic chemicals using information theory. However, not many of these subgraphs are over-represented, thus we use graph pruning techniques to refine and search for toxicophore candidates until enough number of over-represented subgraphs are discovered. Finally, subgraphs are combinatorially combined as toxicophores using frequent pattern mining techniques. The second problem of using gene expression data for cancer subtype and metastasis prediction is formulated as graphs where a graph represents a patient. In the patient graph, nodes are genes and edges are known interactions between genes in the template of protein interaction network. In this formulation, classifying patients is to classify a set of graphs with labels. For the cancer subtype classification, we combined spectral graph learning and relation learning together. For the metastasis prediction for early oral cancer, we used a novel graph reduction techniques using both biological knowledge and gene expression information to create a low dimensional embedding space where patients with and without metastatic potentials are distinguished. Joint work with Drs. Sangsoo Lim, Sungmin Rhee, and Minsoo Kim" 제2회 서울대 AI 여름학교 https://aiis.snu.ac.kr/aisummerschool... 제1회 서울대 AI 여름학교 https://aiis.snu.ac.kr/aisummerschool...