Gene Expression Data Knowledge Discovery using Global and Local Clustering

TL;DR

This paper presents a hybrid clustering approach combining hierarchical k-means and biclustering to analyze gene expression data, enhanced with BLAST searches for biological knowledge discovery and validated by the Figure of Merit.

Contribution

It introduces a hybrid clustering method integrating biclustering and hierarchical k-means with BLAST for biological insights in gene expression analysis.

Findings

Effective identification of local and global clusters

Improved biological knowledge extraction from gene data

Validation confirms clustering quality

Abstract

To understand complex biological systems, the research community has produced huge corpus of gene expression data. A large number of clustering approaches have been proposed for the analysis of gene expression data. However, extracting important biological knowledge is still harder. To address this task, clustering techniques are used. In this paper, hybrid Hierarchical k-Means algorithm is used for clustering and biclustering gene expression data is used. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. A validation technique, Figure of Merit is used to determine the quality of clustering results. Appropriate knowledge is mined from the clusters by embedding a BLAST similarity search program into the clustering and biclustering process. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. To determine the quality of clustering results, a validation technique, Figure of Merit is used. Appropriate knowledge is mined from the clusters by embedding a BLAST similarity search program into the clustering and biclustering process.