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

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 man...

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Bibliographic Details
Published in:IET systems biology Vol. 14; no. 6; pp. 323 - 333
Main Authors: Biswal, Bhawani Sankar, Patra, Sabyasachi, Mohapatra, Anjali, Vipsita, Swati
Format: Journal Article
Language:English
Published: England The Institution of Engineering and Technology 01.12.2020
Subjects:
Algorithms
biology computing
Cluster Analysis
Computational Biology - methods
data analysis
disease identification
diseases
drug discovery
gene expression microarray data clustering
Gene Expression Profiling
gene ontology
genetics
genomics
graph based clustering
graph theory
health care
healthcare research
Humans
KEGG pathway analysis
Oligonucleotide Array Sequence Analysis
ontologies (artificial intelligence)
pattern clustering
Research Article
restricted neighbourhood search clustering
search problems
three dimensional data
triclustering algorithm
TriRNSC algorithm
unsupervised learning
unsupervised machine learning
data analysis
disease identification
graph theory
graph based clustering
KEGG pathway analysis
diseases
healthcare research
unsupervised learning
gene expression microarray data clustering
gene ontology
unsupervised machine learning
restricted neighbourhood search clustering
genetics
biology computing
pattern clustering
drug discovery
genomics
ontologies (artificial intelligence)
three dimensional data
triclustering algorithm
search problems
health care
TriRNSC algorithm
ISSN:1751-8849, 1751-8857, 1751-8857
Online Access:Get full text
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Summary: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.
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ISSN:1751-8849
1751-8857
1751-8857
DOI:10.1049/iet-syb.2020.0024