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 Transition Dependency: A Gene-Gene Interaction Measure for Times Series Microarray Data | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2009 Article ID 535869 12 pages doi 2009 535869 Research Article Transition Dependency A Gene-Gene Interaction Measure for Times Series Microarray Data Xin Gao 1 Daniel Q. Pu 1 and Peter . Song2 1 Department of Mathematics and Statistics York University 4700 Keele Street Toronto ON Canada M3J1P3 2 Department of Biostatistics University of Michigan School of Public Health Ann Arbor MI 48109-2029 USA Correspondence should be addressed to Xin Gao xingao@ Received 1 May 2008 Revised 31 July 2008 Accepted 6 November 2008 Recommended by Dirk Repsilber Gene-Gene dependency plays a very important role in system biology as it pertains to the crucial understanding of different biological mechanisms. Time-course microarray data provides a new platform useful to reveal the dynamic mechanism of genegene dependencies. Existing interaction measures are mostly based on association measures such as Pearson or Spearman correlations. However it is well known that such interaction measures can only capture linear or monotonic dependency relationships but not for nonlinear combinatorial dependency relationships. With the invocation of hidden Markov models we propose a new measure of pairwise dependency based on transition probabilities. The new dynamic interaction measure checks whether or not the joint transition kernel of the bivariate state variables is the product of two marginal transition kernels. This new measure enables us not only to evaluate the strength but also to infer the details of gene dependencies. It reveals nonlinear combinatorial dependency structure in two aspects between two genes and across adjacent time points. We conduct a bootstrapbased X2 test for presence absence of the dependency between every pair of genes. Simulation studies and real biological data analysis demonstrate the application of the proposed method. The software package is .
