Đang chuẩn bị liên kết để tải về tài liệu:
báo cáo hóa học:" Research Article A Hypothesis Test for Equality of Bayesian Network Models"

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 A Hypothesis Test for Equality of Bayesian Network Models | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2010 Article ID 947564 10 pages doi 10.1155 2010 947564 Research Article A Hypothesis Test for Equality of Bayesian Network Models Anthony Almudevar Department of Computational Biology University of Rochester 601 Elmwood Avenue Rochester NY 14642 USA Correspondence should be addressed to Anthony Almudevar anthony_almudevar@urmc.rochester.edu Received 26 March 2010 Revised 9 July 2010 Accepted 5 August 2010 Academic Editor A. Datta Copyright 2010 Anthony Almudevar. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Bayesian network models are commonly used to model gene expression data. Some applications require a comparison of the network structure of a set of genes between varying phenotypes. In principle separately fit models can be directly compared but it is difficult to assign statistical significance to any observed differences. There would therefore be an advantage to the development of a rigorous hypothesis test for homogeneity of network structure. In this paper a generalized likelihood ratio test based on Bayesian network models is developed with significance level estimated using permutation replications. In order to be computationally feasible a number of algorithms are introduced. First a method for approximating multivariate distributions due to Chow and Liu 1968 is adapted permitting the polynomial-time calculation of a maximum likelihood Bayesian network with maximum indegree of one. Second sequential testing principles are applied to the permutation test allowing significant reduction of computation time while preserving reported error rates used in multiple testing. The method is applied to gene-set analysis using two sets of experimental data and some advantage to a pathway modelling approach to .