BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research

Files in This Item:
 File SizeFormat
DownloadBGRMI A method for inferring Santra Kolch Martinez.pdf1.59 MBAdobe PDF
Title: BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research
Authors: Iglesias-Martinez, Luis F.Kolch, WalterSantra, Tapesh
Permanent link:
Date: 23-Nov-2016
Online since: 2018-04-09T12:01:39Z
Abstract: Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.
Funding Details: Irish Cancer Society
Type of material: Journal Article
Publisher: Springer Nature
Journal: Scientific Reports
Volume: 6
Copyright (published version): 2016 the Authors
Keywords: BGRMIGene regulatory networksBreast cancer researchGene regulatory networksGRNsGene expression dataBayesian Gene Regulation Model Inference
DOI: 10.1038/srep37140
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:SBI Research Collection

Show full item record

Citations 20

Last Week
Last month
checked on Sep 12, 2020

Page view(s)

Last Week
Last month
checked on Aug 10, 2022


checked on Aug 10, 2022

Google ScholarTM



If you are a publisher or author and have copyright concerns for any item, please email and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.