Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein-protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.
Pubmed ID: 23525069 RIS Download
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Algorithm for learning parsimonious regulatory networks from systems biology data sets de novo. Software that utilizes inference algorithm to model genetic regulatory networks.Inferelator 2.0 is scalable framework for reconstruction of dynamic regulatory network models.
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