cleanUrl: "bioinfo-2022-john-quackenbush"
description: "BIOINFO 2022 학회의 John Quackenbush (Harvard University) 교수님 기조강연을 정리합니다."
Why does AI struggle?
Inefficient training data
No robust underlying model
The way we train AI is fundamentally flawed (MIT technical report, 2020)
No Free Lunch Theorems for Optimization
Networks beyond simple differences
- Conventional statistical analysis
- Regulatory network를 도입
- 다만, condition-specific regulatory network이 필요하다. (Individual의 차이, cell type의 차이를 반영)
- Analyze network topology and structure
- compare network topologies
- compare structure and expression
- Use individual networks as biomarkers!
- netZoo: An integrated platform
Can we solve the “GWAS Puzzle?”
- Rare variants = Dust?
- eQTL analysis
- Which SNPs are correlated with the degree of gene expression
- Most people concentrate on *cis-*acting SNPs
eQTL Networks
- Standard eQTL analysis
- SNP와 gene의 association을 나타내는 bipartite graph를 구성한다.
- Network의 특성: 하나의 유전자와만 연관된 SNP가 많고, 여러 유전자와 연관된 SNP는 매우 적다 (hubs) (~scale free?)
- 근데 Disease와 연관된 SNP는 hub SNP가 아니다! Hubs are GWAS desert!
- Network comunity modularity에 기여하는 정도를 score로 하여 각 SNP에 할당. (core score)
- Significant SNPs 의 core score는 non-significant SNP보다 median이 20.3배 높더라.