期刊
NATURE GENETICS
卷 40, 期 7, 页码 854-861出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/ng.167
关键词
-
资金
- NIGMS NIH HHS [P50GM071508, P50 GM071508] Funding Source: Medline
- NIMH NIH HHS [R37 MH059520, R37 MH059520-11] Funding Source: Medline
A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据