Apr 22, 2016

Fused regression for multi-source gene regulatory network inference

BioRxiv : the Preprint Server for Biology
Kari Y. LamRichard Bonneau

Abstract

Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

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Mentioned in this Paper

Genes
Cell Differentiation Process
Gene Regulatory Networks
Cell Type
Learning
Species
Plant Extracts
STK36
Inference
Physiological Processes

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