Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India.
IET Syst Biol. 2019 Aug;13(4):194-203. doi: 10.1049/iet-syb.2018.5058.
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.
基因表达数据正被广泛应用于各种临床研究。它同时代表了数千个基因在各种实验条件下的表达水平。从基因表达数据中挖掘特定条件的枢纽基因是一项具有挑战性的任务。特定条件的枢纽基因标志着双聚类在条件子集上的功能行为，并且可以作为疾病的预后或诊断标志物。在这项研究中，作者引入了一种新的方法，使用双聚类算法从 RNA-Seq 数据中识别特定条件的枢纽基因。在所提出的方法中，使用了高效的“runibic”双聚类算法、基因共表达网络的概念和蛋白质-蛋白质相互作用网络的概念，以获得更好的性能。结果表明，所提出的方法提取了具有生物学意义的特定条件的枢纽基因，这些基因在各种生物过程和途径中起着重要作用。这些特定条件的枢纽基因可以用作预后或诊断生物标志物。特定条件的枢纽基因将有助于减少分析时间并提高进一步研究的准确性。此外，他们总结了所提出的方法在药物发现过程中的应用。
