A Bayesian framework that integrates heterogeneous data for inferring gene regulatory networks

Title: A Bayesian framework that integrates heterogeneous data for inferring gene regulatory networks
Authors: Santra, Tapesh
Permanent link: http://hdl.handle.net/10197/9784
Date: 20-May-2014
Online since: 2019-04-03T09:08:19Z
Abstract: Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.
Funding Details: European Commission - Seventh Framework Programme (FP7)
Science Foundation Ireland
Type of material: Journal Article
Publisher: Frontiers Media
Journal: Frontiers in Bioengineering and Biotechnology
Volume: 2
Start page: 1
End page: 14
Copyright (published version): 2014 the Author
Keywords: Network inferenceBayesian statisticsData interpretationStatisticalVariable selectionGene regulatory networks
DOI: 10.3389/fbioe.2014.00013
Language: en
Status of Item: Peer reviewed
Appears in Collections:SBI Research Collection

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