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 Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2009 Article ID 545176 13 pages doi 2009 545176 Research Article Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data Mingzhou Joe Song 1 Chris K. Lewis 1 Eric R. Lance 1 Elissa J. Chesler 2 Roumyana Kirova Yordanova 3 Michael A. Langston 4 Kerrie H. Lodowski 5 and Susan E. Bergeson6 1 Department of Computer Science New Mexico State University Las Cruces NM 88003 USA 2 Systems Genetics Group Biosciences Division Oak Ridge National Laboratory Oak Ridge TN 37831 USA 3 Department of Applied Genomics Bristol-Myers Squibb R D . Box 5400 Princeton NJ 08543 USA 4 Department of Computer Science University of Tennessee Knoxville TN 37996 USA 5 Department of Pharmacology School of Medicine Case Western Reserve University Cleveland OH 44106 USA 6 Department of Pharmacology and Neuroscience Texas Tech University Lubbock TX 79430 USA Correspondence should be addressed to Mingzhou Joe Song joemsong@ Received 1 June 2008 Revised 8 September 2008 Accepted 12 December 2008 Recommended by Dirk Repsilber Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of
