Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1004504
Title: Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks
Authors: Narang V.
Ramli M.A.
Singhal A.
Kumar P.
de Libero G.
Poidinger M. 
Monterola C.
Keywords: Article
autoanalysis
data analysis
gene function
gene identification
gene regulatory network
gene targeting
genetic algorithm
human genetics
mathematical analysis
MCF 7 cell line
microarray analysis
molecular evolution
transcription regulation
algorithm
biology
breast tumor
classification
female
gene expression profiling
gene regulatory network
genetic database
genetics
human
metabolism
procedures
tumor cell line
estrogen
tumor marker
Algorithms
Biomarkers, Tumor
Breast Neoplasms
Cell Line, Tumor
Computational Biology
Databases, Genetic
Estrogens
Female
Gene Expression Profiling
Gene Regulatory Networks
Humans
Issue Date: 2015
Citation: Narang V., Ramli M.A., Singhal A., Kumar P., de Libero G., Poidinger M., Monterola C. (2015). Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks. PLoS Computational Biology 11 (9) : e1004504. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1004504
Abstract: Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript. ? 2015 Narang et al.
Source Title: PLoS Computational Biology
URI: https://scholarbank.nus.edu.sg/handle/10635/161933
ISSN: 1553734X
DOI: 10.1371/journal.pcbi.1004504
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