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TriRNSC: triclustering of gene expression microarray data using restricted neighbourhood search

Content Provider	IET Digital Library
Author	Biswal, Bhawani Sankar Patra, Sabyasachi Mohapatra, Anjali Vipsita, Swati
Abstract	Computational analysis of microarray data is crucial for understanding the gene behaviours and deriving meaningful results. Clustering and biclustering of gene expression microarray data in the unsupervised domain are extremely important as their outcomes directly dominate healthcare research in many aspects. However, these approaches fail when the time factor is added as the third dimension to the microarray datasets. This three-dimensional data set can be analysed using triclustering that discovers similar gene sets that pursue identical behaviour under a subset of conditions at a specific time point. A novel triclustering algorithm (TriRNSC) is proposed in this manuscript to discover meaningful triclusters in gene expression profiles. TriRNSC is based on restricted neighbourhood search clustering (RNSC), a popular graph-based clustering approach considering the genes, the experimental conditions and the time points at an instance. The performance of the proposed algorithm is evaluated in terms of volume and some performance measures. Gene Ontology and KEGG pathway analysis are used to validate the TriRNSC results biologically. The efficiency of TriRNSC indicates its capability and reliability and also demonstrates its usability over other state-of-art schemes. The proposed framework initiates the application of the RNSC algorithm in the triclustering of gene expression profiles.
Starting Page	323
Ending Page	333
Page Count	11
ISSN	17518849
Volume Number	14
e-ISSN	17518857
Issue Number	Issue 6, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-syb/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-syb.2020.0024
Journal	IET Systems Biology
Publisher Date	2020-08-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Artificial Intelligence Biology And Medical Computing Biology Computing Combinatorial Mathematics Data Analysis Data Handling Technique Disease Identification Diseases Drug Discovery Gene Expression Microarray Data Clustering Gene Ontology Genetics Genomics Graph Based Clustering Graph Theory Healthcare Healthcare Research KEGG Pathway Analysis Knowledge Engineering Technique Ontologies Pattern Clustering Restricted Neighbourhood Search Clustering Search Problem Three Dimensional Data Triclustering Algorithm TriRNSC Algorithm Unsupervised Learning Unsupervised Machine Learning
Content Type	Text
Resource Type	Article
Subject	Cell Biology Genetics Molecular Biology Modeling and Simulation Biotechnology

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