Triclustering of Gene Expression Microarray Data Using Coarse-Grained Parallel Genetic Algorithm
This work addresses the need for improved triclustering methods in computational biology to enhance disease identification and drug discovery, though it appears incremental as it builds on existing genetic approaches.
The authors tackled the problem of analyzing gene expression microarray data when time is a factor by proposing a coarse-grained parallel genetic algorithm for triclustering, achieving more effective results compared to traditional state-of-the-art genetic approaches.
Microarray data analysis is one of the major area of research in the field computational biology. Numerous techniques like clustering, biclustering are often applied to microarray data to extract meaningful outcomes which play key roles in practical healthcare affairs like disease identification, drug discovery etc. But these techniques become obsolete when time as an another factor is considered for evaluation in such data. This problem motivates to use triclustering method on gene expression 3D microarray data. In this article, a new methodology based on coarse-grained parallel genetic approach is proposed to locate meaningful triclusters in gene expression data. The outcomes are quite impressive as they are more effective as compared to traditional state of the art genetic approaches previously applied for triclustering of 3D GCT microarray data.