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Báo cáo hóa học: " Research Article Analysis of Gene Coexpression by B-Spline Based CoD Estimation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Analysis of Gene Coexpression by B-Spline Based CoD Estimation | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007 Article ID 49478 10 pages doi 10.1155 2007 49478 Research Article Analysis of Gene Coexpression by B-Spline Based CoD Estimation Huai Li Yu Sun and Ming Zhan Bioinformatics Unit Branch of Research Resources National Institute on Aging National Institutes of Health Baltimore MD 21224 USA Received 31 July 2006 Revised 3 January 2007 Accepted 6 January 2007 Recommended by Edward R. Dougherty The gene coexpression study has emerged as a novel holistic approach for microarray data analysis. Different indices have been used in exploring coexpression relationship but each is associated with certain pitfalls. The Pearson s correlation coefficient for example is not capable of uncovering nonlinear pattern and directionality of coexpression. Mutual information can detect nonlinearity but fails to show directionality. The coefficient of determination CoD is unique in exploring different patterns of gene coexpression but so far only applied to discrete data and the conversion of continuous microarray data to the discrete format could lead to information loss. Here we proposed an effective algorithm CoexPro for gene coexpression analysis. The new algorithm is based on B-spline approximation of coexpression between a pair of genes followed by CoD estimation. The algorithm was justified by simulation studies and by functional semantic similarity analysis. The proposed algorithm is capable of uncovering both linear and a specific class of nonlinear relationships from continuous microarray data. It can also provide suggestions for possible directionality of coexpression to the researchers. The new algorithm presents a novel model for gene coexpression and will be a valuable tool for a variety of gene expression and network studies. The application of the algorithm was demonstrated by an analysis on ligand-receptor coexpression in cancerous and noncancerous cells. The software .