title: Gene interaction networks based on kernel correlation metrics.
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.
authors: College of Information Sciences and Technology, Donghua University, Shanghai, China. cljcathy@126.com
authors: Lijun
authors: L
authors: Cheng
authors: K
authors: K
authors: Khorasani
authors: Yongsheng
authors: Y
authors: Ding
authors: Xihong
authors: X
authors: Guo
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012372:ROC Curve; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism