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 Inference of a Probabilistic Boolean Network from a Single Observed Temporal Sequence | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007 Article ID 32454 15 pages doi 2007 32454 Research Article Inference of a Probabilistic Boolean Network from a Single Observed Temporal Sequence Stephen Marshall 1 Le Yu 1 Yufei Xiao 2 and Edward R. Dougherty2 3 4 1 Department of Electronic and Electrical Engineering Faculty of Engineering University of Strathclyde Glasgow G1 1XW UK 2 Department of Electrical and Computer Engineering Texas A M University College Station TX 77843-3128 USA 3 Computational Biology Division Translational Genomics Research Institute Phoenix AZ 85004 USA 4 Department of Pathology University of Texas M. D. Anderson Cancer Center Houston TX 77030 USA Received 10 July 2006 Revised 29 January 2007 Accepted 26 February 2007 Recommended by Tatsuya Akutsu The inference of gene regulatory networks is a key issue for genomic signal processing. This paper addresses the inference of probabilistic Boolean networks PBNs from observed temporal sequences of network states. Since a PBN is composed of a finite number of Boolean networks a basic observation is that the characteristics of a single Boolean network without perturbation may be determined by its pairwise transitions. Because the network function is fixed and there are no perturbations a given state will always be followed by a unique state at the succeeding time point. Thus a transition counting matrix compiled over a data sequence will be sparse and contain only one entry per line. If the network also has perturbations with small perturbation probability then the transition counting matrix would have some insignificant nonzero entries replacing some or all of the zeros. If a data sequence is sufficiently long to adequately populate the matrix then determination of the functions and inputs underlying the model is straightforward. The difficulty comes when the transition counting matrix consists of data derived from more than one Boolean .
