title: Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.
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.
authors.affiliation: Singapore Immunology Network, Singapore.
authors.forename: Vipin
authors.initials: V
authors.lastname: Narang
authors.affiliation: Institute of High Performance Computing, Singapore.
authors.forename: Muhamad Azfar
authors.initials: MA
authors.lastname: Ramli
authors.affiliation: Singapore Immunology Network, Singapore.
authors.forename: Amit
authors.initials: A
authors.lastname: Singhal
authors.affiliation: Singapore Immunology Network, Singapore.
authors.forename: Pavanish
authors.initials: P
authors.lastname: Kumar
authors.affiliation: Singapore Immunology Network, Singapore.
authors.forename: Gennaro
authors.initials: G
authors.lastname: de Libero
authors.affiliation: Singapore Immunology Network, Singapore.
authors.forename: Michael
authors.initials: M
authors.lastname: Poidinger
authors.affiliation: Institute of High Performance Computing, Singapore.
authors.forename: Christopher
authors.initials: C
authors.lastname: Monterola
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000145:classification / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D004967:Estrogens; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans
chemical_list: D014408:Biomarkers, Tumor; D004967:Estrogens