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

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

SUBMITTER: Biswal BS 

PROVIDER: S-EPMC8687346 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature