SGD Paper 
Zhu J, et al. (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40(7):854-61
Abstract: 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.
| Status: Published | Type: Journal Article | PubMed ID: 18552845 |
Topics addressed in this paper
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| Topics | Topics not linked to Genes |
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| Computational analysis | 
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| Omics |
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