Bayesian Networks for Expression References

• Statistical Genomics References
  • Bibliography on Learning Causal Networks of gene interactions, F Markowetz (2004)
  • Gene Network References
  • Network References in Alphabetical Order (below)
  • Expression Network Software
  • QTL References
  • Microarray References

  ---

• Software Packages
  • APID
  • Cell Illustrator (Genomic Object Net)
  • CytoScape
  • Deal
  • DIP
  • DAVID (pathway inference)
  • FatiGO
  • GeneText
  • GeneXPress
  • Gepasi
  • GOstat
  • GraphExplore
  • Ingenuity (pathway inference)
  • MAPPFinder
  • MetaGeneCreator
  • MicroDiscovery
  • Pathway Assist
  • Affymetrix Integrated Genome Browser (IGB)
  • TransVar, U AL Birmingham
  • WholePathwayScope [Paper]
• Genome Informatics
  • Mouse Genome Informatics (MGI)
    • MGI Prototypes
  • Gene Ontology (GO)
  • Kyoto Encyclopedia of Genes and Genomes (KEGG)
  • information Hyperlinked Over Proteins (iHOP)
  • Bruce Aranow, U Cincinnati/CHMC:
    • Genome Transcription Factors
    • Transcription Factor Homology Server
    • PolyDoms: Mapping of Human Coding SNPs onto Protein Domains
    • Cis-regulatory Modules
  • WebGestalt

Network References in Alphabetical Order

• Aerts S, Van Loo P, Thijs G, Moreau Y, De Moor B (2003) Computational detection of cis-regulatory modules. Bioinformatics 19: 5-14. [Paper]
• Aerts S, Van Loo P, Moreau Y, and De Moor B (2004) A genetic algorithm for the detection of new cis-regulatory modules in sets of coregulated genes. Bioinformatics 20: 1974-1976. [Paper]
• Al-Shahrour F, Diaz-Uriarte R, Dopazo J (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20: 578-580. [Paper]

• Balling R (2007) From mouse genetics to systems biology. Mammal Genome 18: 383-388. [doi:10.1007/s00335-007-9044-2]
• Beiβbarth T, Speed TP (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20: 1464-1465. [Paper]
• Bittner M, Meltzer P, Trent J (1999) Data analysis and integration: of steps and arrows. Nat Genet. 22: 213-5. [doi:10.1038/10265]
• Bluthgen N, Brand K, Cajavec B, Swat, Herzel H, Beule D (2004) Biological profiling of gene groups utilizing gene ontology--a statistical framework. xxx.arxiv.org/archive/q-bio. [Paper | Software]
• Bottcher SG, Dethlefsen C (2003) deal: A package for learning Bayesian networks. J Statist Soft 8: 1-40. [Paper | Software]]
• Brazhnik P, de la Fuente A, Mendes P (2002) Gene networks: how to put the function in genomics. Trends Biotechnol 20: 467-472. [doi:10.1016/S0167-7799(02)02053-X]

• Can T, Camoglu O, Singh AK (2005) Analysis of protein-protein interaction networks using random walks. Proc 5th ACM SIGKDD Workshop on Data Mining in Bioinfo, August, 2005. [Paper]
• Carvalho CM, Lucas J, Wang Q, Nevins J, West M (2008) High-dimensional sparse factor modelling: applications in gene expression genomics. JASA (to appear). [Paper]
• Chang JH, Hwang KB, O SJ, Zhang BT (2005) Bayesian network learning with feature abstraction for gene-drug dependency analysis. J Bioinfo Comput Biol 3: 61-77. [doi:10.1142/S0219720005000874]
• Chang JH, Hwang KB, Zhang BT (2002) Analyis of gene expression profiles and drug activity patterns by clustering and Bayesian network learning. Methods of Microarray Data Analysis II (Proc CAMDA'01): 169-184.
• Chaibub Neto E, Ferrara C, Attie AD, Yandell BS (2008) Inferring causal phenotype networks from segregating populations. Genetics 179: 1089-1100. [doi:10.1534/genetics.107.085167]
• Chipman KC, Singh AK (2009) Predicting genetic interactions with random walks on biological networks. BMC Bioinfo 10: 17. [doi:10.1186/1471-2105-10-17]
• Conti DV, Cortessis V, Molitor J, Thomas DC (2003) Bayesian modeling of complex metabolic pathways. Hum Hered 56: 83-93. [doi:10.1159/000073736]

• de la Fuente A, Bing N, Hoeschele I, Mendes P (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20: 3565-3574. [doi:10.1093/bioinformatics/bth445]
• de la Fuente A, Brazhnik P, Mendes P (2002) Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet 18: 395-398. [doi:10.1016/S0168-9525(02)02692-6]
• Dhaseleer P, Liang S, Somogoyi R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16: 707-726. [Paper]
• Dittrich MT, Klau GW, Rosenwald A, Dandekar T, Muller T (2008) Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinfo 24: i223-i231. [doi:10.1093/bioinformatics/btn161]
• Dobra A, West M (2004) Graphical model-based gene clustering and metagene expression analysis. [Paper | Software]
• Dobra A, Jones B, Hans C, Nevins JR, West M (2004) Sparse graphical models for exploring gene expression data. J. Mult Analysis 90: 196-212. [doi:10.1016/j.jmva.2004.02.009]
• Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BC (2003) MAPPFinder: using Gene Ontology and GeneMAPP to create a global gene-expression profile from microarray data. Genome Biol 4: R7. [doi:10.1186/gb-2003-4-1-r7]
• Draghici S, Khatri P, Dahlquist KD, Martins RP, Ostermeir GC, Krawetz SA (2003) Global functional profiling of gene expression. Genomics 81: 98-104. [doi:10.1016/S0888-7543(02)00021-6]

• Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303: 799-805. Review. [doi:10.1126/science.1094068]
• Friedman N, Linial M, Nachman I, Pe'er D (2000) Using Bayesian networks to analyze expression data. J Comp Biol 7: 601-620. [doi:10.1089/106652700750050961]

• Gardner TS, di Bernardo D, Lorenz D, Collins J (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301: 102-105. [doi:10.1126/science.1081900]

• Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, et al. (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430: 88-93. [doi:10.1038/nature02555]
• Hanisch D, Zien A, Zimmer R, Lengauer T (2002) Co-clustering of biological networks and gene expression data. Bioinformatics 18 Suppl 1: S145-S154. [Paper]
• Hartemink A, Gifford D, Jaakkola T, Young R (2001) Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pacific Symposium on Biocomputing (PSB01): 422-433. [Paper]
• Hartemink AJ, Gifford DK, Jaakkola TS, Young R (2002) Combining location and expression data for principled discovery of genetic regulatory networks. Pacific Symposium on Biocomputing (PSB02): 437-449. [Paper]
• Henderson DA, de la Fuente A, Mendes P, Hoeschele I (2004) Maximum likelihood and Bayesian factor analysis for the identification of metabolic pathways from microarray expression data. (in revision?) [Author]
• Hernandez-Toro J, Prieto C, De Las Rivas J (2007) APID2NET: unified interactome graphic analyzer. Bioinfo 23: 2495-2497. [doi:10.1093/bioinformatics/btm373 | Software]
• Huang E, Ishida S, Pittman J, Dressman H, Bild A, D'Amico M, Pestell R, West M, Nevins JR (2003) Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet 34: 226-130. [doi:10.1038/ng1167]
• Hwang KB (2002) Microarray data analysis using Bayesian networks. [PPT Talk]
• Hwang KB, Kong SW, Greenberg SA, Park PJ (2004) Combining gene expression data from different generations of oligonucleotide arrays. (submitted) [Author]
• Hwang KB, Zhang BT (2004) Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets. (submitted) [Author]

• Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N (2002) Revealing modular organization in the yeast transcriptional network. Nat Genet 31: 370-377. [doi:10.1038/ng941]

• Keles S, van der Laan MJ, Vulpe C (2004) Regulatory motif finding by logic regression Bioinformatics 20: 2799-2811. [Paper]
• Kim C, Cheon M, Kang M, Chang I (2008) A simple and exact Laplacian clustering of complex networking phenomena: Application to gene expression profiles. PNAS 105: 4083-4087. [doi:10.1073/pnas.0708598105]

• Lee I, Date SV, Adai AT, Marcotte EM (2004) A probabilistic functional network of yeast genes. Science 306: 1555-1558. [doi:10.1126/science.1099511]
• Lee SI, Pe'er D, Dudley AM, Church GM, Koller D (2006) Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. PNAS 103: 14062-14067. [doi:10.1073/pnas.0601852103]
• Li LM, Lu HHS (2005) Explore biological pathways from noisy array data by directed acyclic boolean networks. J Comp Biol 12: 170-185. [doi:10.1089/cmb.2005.12.170]

• Markowetz F (2004) A bibliography on learning causal networks of gene interactions. [Paper]
• Markowetz F, Spang R (2003) Reconstructing gene regulation networks from passive observations and active interventions. 7th Ann Intl Conf Res Comput Molec Biol (RECOMB): poster. [Paper]
• Mendes P (1993) GEPASI: a software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Appl Biosci 9: 563-571. [Abstract | Software]
• Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34:267-73. [doi:10.1038/ng1180]

• Nachman I, Regev A, Friedman N (2004) Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20 Suppl 1: I248-I256. [Paper]

• Ong IM, Glasner J, Page D (2002) Modeling regulatory pathways in E. coli from time series expression profiles. Bioinformatics 18: 241-248. [Paper]
• Opgen-Rhein R, Strimmer K (2007) From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Systems Biol 1: 39. [doi:10.1186/1752-0509-1-39]
• Owen AB, Stuart J, Mach K, Villeneuve AM, Kim S (2003) A gene recommender algorithm to identify coexpressed genes in C. elegans. Genome Res 13: 1828-1837. [doi:10.1101/gr.1125403]

• Pavlidis P, Lewis DP, Noble WS (2002) Exploring gene expression data with class scores. Proc Pac Symp Biocomp: 474-485. [Paper]
• Pearl J (1995) Causal diagrams for empirical research. Biometrika 82: 669-710. With discussion. [doi:10.1093/biomet/82.4.669]
• Pearl J (2000) Causality. Cambridge University Press, Cambridge. [Book]
• Pearl J (2003) Statistics and causal inference: a review. Test 12: 281-318. [Paper]
• Pe'er D, Regev A, Elidan G, Friedman N (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 Suppl 1: S215-S224. [Paper]
• Pe'er D, Regev A, Tanay A (2002) Minreg: inferring an active regulator set. Bioinformatics 18 Suppl 1: S258-S267 [Paper]
• Prieto C, De Las Rivas J (2006) APID: Agile Protein Interaction DataAnalyzer. Nucleic Acids Res 34: W298-W302. [doi:10.1093/nar/gkl128 | Software]

• Rahnenführer J, Domingues FS, Maydt J, Lengauer T (2004) Calculating the statistical significance of changes in pathway activity from gene expression data. Statist Appl in Genet Molec Biol 13: article 16. [Paper]
• Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297: 1551-1555. [doi:10.1126/science.1073374]

• Schadt EE, Monks SA, Friend SH (2003) A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets. Biochem Soc Trans. 31: 437-43. Review. [Paper]
• Segal E, Barash Y, Simon I, Friedman N, Koller D (2002) From promoter sequence to expression: a probabilistic framework. Proc. 6th Inter. Conf. on Research in Computational Molecular Biology (RECOMB), Washington, DC. [Paper]
• Segal E, Sharan R (2004) A discriminative model for identifying spatial cisregulatory modules. Proc. 8th Inter. Conf. on Research in Computational Molecular Biology (RECOMB), San-Diego, CA [Paper]
• Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 34: 166-76. [doi:10.1038/ng1165 ]
• Segal E, Wang H, Koller D (2003) Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19 Suppl 1: i264-i272. [Paper]
• Sinha S, van Nimwegen E, Siggia ED (2003) A probabilistic method to detect regulatory modules. Bioinformatics 19: 292-301. [Paper]
• Smith VA, Jarvis ED, Hartemink AJ (2002) Evaluating functional network inference using simulations of complex biological experiments. Bioinformatics 18 suppl 1: S216-S224. [Paper]
• Sohler F, Hanisch D, Zimmer R (2004) New methods for joint analysis of biological networks and expression data. Bioinformatics 20: 1517-1521. [Paper]
• Spirtes P, Glymour C, Scheines R (2000) Causation, Prediction, and Search, 2nd ed. Adaptive Computation and Machine Learning. MIT Press, Cambridge. [Book]
• Stuart J, Segal E, Koller D, Kim S (2003) A gene co-expression network for global discovery of conserved genetics modules. Science 302: 249-55. [doi:10.1126/science.1087447]
• Stumpf M, Thorne T, de Silva E, Stewart R, An HJ, Lappe M, Wiuf C (2008) Estimating the size of the human interactome. PNAS 105: 6959-6964. [doi:10.1073/pnas.0708078105]

• Tanay A, Shamir R (2001) Computational expansion of genetic networks. Bioinformatics 17 Suppl 1: S270-S278. [Paper]
• Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) Systematic determination of genetic network architecture. Nat Genet. 22: 281-5. [doi:10.1038/10343]

• Vert JP, Kanehisa M (2003) Extracting active pathways from gene expression data. Bioinformatics 19 Suppl 2: ii238-ii244. [Paper]

• Wagner A (2001) How to reconstruct a large genetic network from n gene perturbations in fewer than n² easy steps. Bioinformatics 17: 1183-1197. [Paper]
• Walczak AM, Sasai M, Wolynes PG (2004) Self consistent proteomic field theory of stochastic gene switches. xxx.arxiv.org/archive/q-bio. [Paper]
• Wang Q, Dobra A, West M (2004) GraphExplore: A software tool for network visualization. [Paper | Software]

• Xenarios I, Salwinski L, Duan XJ, Higney P, Kim S, Eisenberg D (2002) DIP: The Database of Interacting Proteins. A research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30: 303-305. [doi:10.1093/nar/30.1.303 | Software]

• Zhang BT, Hwang KB (2003) Bayesian network classifiers for gene expression analysis. A Practical Approach to Microarray Data Analysis, Berrar, D.P., Dubitzky, W., and Granzow, M. (eds.), pp. 150-165, Kluwer Academic Publishers. [Author]
• Zhou Q, Wong WH (2004) CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling. PNAS 101: 12114-12119. [Paper]
• Zhou X, Kao MCJ, Wong WH (2002) From the Cover: Transitive functional annotation by shortest-path analysis of gene expression data PNAS 99: 12783-12788. [Paper]
• Zhu J, Lum PY, Lamb J, GuhaThakurta D, Edwards SW, Thieringer R, Berger JP, Wu MS, Thompson J, Sachs AB, Schadt EE (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res. 105: 363-74. [doi:10.1159/000078209]
• Zien A, Küffner R, Lengauer T (2000) Analysis of gene expression data with pathway scores. Proc Intl Conf Intell Syst Mol Biol 8: 407-417. [Abstract]