CNNC: a common nearest neighbour clustering approach for gene expression data. (6th September 2011)

• Record Type: Journal Article
• Authors: Goswami, Mausumi; Sarmah, Rosy; Bhattacharyya, D.K.
• Is Part Of: International journal of computational vision and robotics. Volume 2:Number 2(2011)
• Page Start: 115
• Page End: 126
• Publication Date: 2011-09-06
• Subjects: common nearest neighbour -- Pearson correlation coefficient -- regulation pattern -- gene expression data -- CNN clustering -- clusters -- bioinformatics; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

We present an effective common nearest neighbour-based clustering technique (CNNC) for finding clusters over gene expression data. CNNC attempts to find all the clusters over gene expression data qualitatively. Our algorithm works by finding clusters using a nearest neighbour-based approach. A regulation-based module for finding sub clusters is also presented here. CNNC was tested on several real-life datasets and the effectiveness is established in terms of well known z-score measure and p-value over several real-life datasets. Using z-score analysis we show that CNNC outperforms other comparable algorithms. The p-value analysis shows that our technique is capable in detecting biologically relevant clusters from gene expression data.