Open AccessMining conditions specific hub genes from RNA‐Seq gene‐expression data via biclustering and their application to drug discovery
Author(s) -
Maind Ankush,
Raut Shital
Publication year - 2019
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2018.5058
Subject(s) - biclustering , gene , rna seq , computational biology , drug discovery , gene co expression network , data mining , expression (computer science) , computer science , gene expression , cluster analysis , biology , bioinformatics , genetics , machine learning , transcriptome , gene ontology , cure data clustering algorithm , correlation clustering , programming language
Gene‐expression data is being widely used for various clinical research. It represents expression levels of thousands of genes across the various experimental conditions simultaneously. Mining conditions specific hub genes from gene‐expression data is a challenging task. Conditions specific hub genes signify the functional behaviour of bicluster across the subset of conditions and can act as prognostic or diagnostic markers of the diseases. In this study, the authors have introduced a new approach for identifying conditions specific hub genes from the RNA‐Seq data using a biclustering algorithm. In the proposed approach, efficient ‘runibic’ biclustering algorithm, the concept of gene co‐expression network and concept of protein–protein interaction network have been used for getting better performance. The result shows that the proposed approach extracts biologically significant conditions specific hub genes which play an important role in various biological processes and pathways. These conditions specific hub genes can be used as prognostic or diagnostic biomarkers. Conditions specific hub genes will be helpful to reduce the analysis time and increase the accuracy of further research. Also, they summarised application of the proposed approach to the drug discovery process.