Copyright © 2006 by Devika Subramanian

These materials are for educational use by members of the Rice Computer Science Department. Use for other purposes requires permission of the author.

Bayesian networks and their applications

Bayesian networks: the theory

• A tutorial on learning with bayesian networks , D. Heckerman, Microsoft.
• Learning Probabilistic Relational Models , L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Invited contribution to the book Relational Data Mining, S. Dzeroski and N. Lavrac, Eds., Springer-Verlag, 2001.
• Bayes nets fundamentals

Background reading on genetic networks

• Modeling and simulation of genetic regulatory networks, H.De Jong, Journal of Computational Biology, 9(1):67-103, 2002.
• Transcriptional regulatory networks in Saccharomyces cerevisiae, Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) Science 298, 799-804.
• Global mapping of the yeast genetic interaction network, Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, Chen Y, Cheng X, Chua G, Friesen H, Goldberg DS, Haynes J, Humphries C, He G, Hussein S, Ke L, Krogan N, Li Z, Levinson JN, Lu H, Menard P, Munyana C, Parsons AB, Ryan O, Tonikian R, Roberts T, Sdicu AM, Shapiro J, Sheikh B, Suter B, Wong SL, Zhang LV, Zhu H, Burd CG, Munro S, Sander C, Rine J, Greenblatt J, Peter M, Bretscher A, Bell G, Roth FP, Brown GW, Andrews B, Bussey H, Boone C, Science. 2004 Feb 6;303(5659):808-13.

Inferring regulatory networks from genomic and proteomic data

• Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data, Tanay A, Sharan R, Kupiec M, Shamir R, Proc Natl Acad Sci U S A. 2004 Mar 2;101(9):2981-6. Epub 2004 Feb 18.
• Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks, Nariai N, Kim S, Imoto S, Miyano S, Pac Symp Biocomput. 2004;:336-47.
• Module Networks: Discovering Regulatory Modules and their Condition Specific Regulators from Gene Expression Data, E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller, N. Friedman Nature Genetics, 2003 June, 34(2): 166-76. Supplement to paper .
• Genome-wide Discovery of Transcriptional Modules from DNA Sequence and Gene Expression, E. Segal, R. Yelensky, D. Koller Bioinformatics, 2003; 19 Suppl 1.
• Discovering Molecular Pathways from Protein Interaction and Gene Expression Data, E. Segal, H. Wang, D. Koller Bioinformatics, 2003; 19 Suppl 1.
• Decomposing Gene Expression into Cellular Processes, E. Segal, A. Battle, D. Koller In Proceedings of the 8th Pacific Symposium on Biocomputing (PSB), Kaua'i, January 2003.
• Rich Probabilistic Models for Gene Expression, E. Segal, B. Taskar, A. Gasch, N. Friedman, D. Koller Bioinformatics, 2003; 17 Suppl 1:S243-252
• Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection, Tamada Y, Kim S, Bannai H, Imoto S, Tashiro K, Kuhara S, Miyano S, Bioinformatics. 2003 Oct;19 Suppl 2:II227-II236.
• Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks,Shmulevich I, Dougherty ER, Kim S, Zhang W., Bioinformatics. 2002 Feb;18(2):261-74.
• Inferring subnetworks from perturbed expression profiles, Pe'er D, Regev A, Elidan G, Friedman N, Bioinformatics. 2001;17 Suppl 1:S215-24.
• Using Bayesian networks to analyze expression data, Friedman N, Linial M, Nachman I, Pe'er D. J Comput Biol. 2000;7(3-4):601-20.

Bayesian network software

• Bayes Net Toolbox for Matlab, Kevin Murphy, MIT.
• MSBNx, Bayes net software. and WinMine , both from Microsoft.
• Genie and Smile (C++ software for building and learning bayesian networks from data), University of Pittsburgh.
• LibB, learning Bayes networks from data for Windows and Linux platforms (from N. Friedman's group at Hebrew University).
• Causal Explorer: A Probabilistic Network Learning Toolkit for Biomedical Discovery, C. Aliferis, I. Tsamardinos, A. Statnikov. International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS), 2003.[download] (Matlab under Win 32)
• Tetrad, from Glymour's group at CMU.

Other useful software and data for exploring metabolic networks and gene expression data

• GeneXPress is a visualization and analysis tool for gene expression data, integrating clustering, gene annotation, and sequence information.
• SGD is a scientific database of the molecular biology and genetics of the yeast Saccharomyces cerevisiae.
• KEGG: Kyoto Encyclopedia of Genes and Genomes
• Gene Ontology Consortium