Authors: Lijun Cheng; K. Khorasani; Yongsheng Ding; Xihong Guo
Addresses: College of Information Sciences and Technology, Donghua University, Shanghai 201620, China ' Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, H3G 1M8 Canada ' College of Information Sciences and Technology, Donghua University, Shanghai 201620, China; Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China ' Department of Pharmacy, People Hospital of Xinjiang Autonomous Region, Urumqui 830001, China
Abstract: In this paper, a Kernel correlation coefficient (KCC) method is proposed to elucidate the gene nonlinear relationships as a distance metric. To evaluate the performance of this nonlinear distance measure, a biological network of the Gaussian Kernel on a public dataset of yeast genes is constructed by using a graph theory. Specifically, the distribution and properties of this new measure are analysed and compared with the classical Pearson correlation method. The reliability and advantages of our proposed Kernel correlation metric is verified and shown formally on ten showcases of the DREAM (Dialogue for Reverse Engineering Assessments and Methods) project. Test experiment results demonstrate that the proposed Kernel correlation coefficient measure has a strong capability in identifying interaction genes, and that the proposed method can detect accurately the key genes and functional interactions (also known as the cliques) as compared to the commonly used Pearson correlation and Mutual Information measures.
Keywords: gene interaction networks; KCC; kernel correlation coefficient; Pearson correlation; yeast genes network; Saccharomyces cerevisiae; functional interactions; cliques.
International Journal of Computational Biology and Drug Design, 2013 Vol.6 No.1/2, pp.72 - 92
Published online: 20 Feb 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article