{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T05:20:52Z","timestamp":1674710452116},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures.<\/jats:p>\n               <jats:p>Results: We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a \u2018search and score\u2019 network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and Bayesian network methods using four benchmark datasets from DREAM. In our final study, we showcased two context-specific signaling pathways activated in breast cancer.<\/jats:p>\n               <jats:p>Availibility: Source codes are available from http:\/\/dl.dropbox.com\/u\/16000775\/sa_sc.zip<\/jats:p>\n               <jats:p>Contact: \u00a0dzhu@wayne.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr696","type":"journal-article","created":{"date-parts":[[2011,12,24]],"date-time":"2011-12-24T02:37:52Z","timestamp":1324694272000},"page":"546-556","source":"Crossref","is-referenced-by-count":6,"title":["Optimal structural inference of signaling pathways from unordered and overlapping gene sets"],"prefix":"10.1093","volume":"28","author":[{"given":"Lipi R.","family":"Acharya","sequence":"first","affiliation":[{"name":"1 Department of Computer Science, University of New Orleans, New Orleans, LA 70148, 2Department of Computer Science, Wayne State University, Detroit, MI 48202 and 3Department of Chemistry, Xavier University of Louisiana, New Orleans, LA 70125, USA"}]},{"given":"Thair","family":"Judeh","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, University of New Orleans, New Orleans, LA 70148, 2Department of Computer Science, Wayne State University, Detroit, MI 48202 and 3Department of Chemistry, Xavier University of Louisiana, New Orleans, LA 70125, USA"}]},{"given":"Guangdi","family":"Wang","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, University of New Orleans, New Orleans, LA 70148, 2Department of Computer Science, Wayne State University, Detroit, MI 48202 and 3Department of Chemistry, Xavier University of Louisiana, New Orleans, LA 70125, USA"}]},{"given":"Dongxiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, University of New Orleans, New Orleans, LA 70148, 2Department of Computer Science, Wayne State University, Detroit, MI 48202 and 3Department of Chemistry, Xavier University of Louisiana, New Orleans, LA 70125, USA"}]}],"member":"286","published-online":{"date-parts":[[2011,12,22]]},"reference":[{"key":"2023012512182748100_B1","article-title":"GSGS: a computaional framework to reconstruct signaling pathways from gene sets","author":"Acharya","year":"2011","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"2023012512182748100_B2","volume-title":"Molecular Biology of the Cell","author":"Alberts","year":"2002","edition":"4th"},{"key":"2023012512182748100_B3","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1093\/bioinformatics\/btq259","article-title":"Revealing differences in gene network inference algorithms on the network-level by ensemble methods","volume":"26","author":"Altay","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012512182748100_B4","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1186\/1752-0509-4-132","article-title":"Inferring the conservative causal core of gene regulatory networks","volume":"4","author":"Altay","year":"2010","journal-title":"BMC Syst. Biol."},{"key":"2023012512182748100_B5","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/1745-6150-6-31","article-title":"Structural influence of gene networks on their inference: analysis of C3NET","volume":"6","author":"Altay","year":"2011","journal-title":"Biol. Direct."},{"key":"2023012512182748100_B6","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1093\/bioinformatics\/btg402","article-title":"LVB: parsimony and simulated annealing in the search for phylogenetic trees","volume":"20","author":"Baker","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012512182748100_B7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.mce.2009.02.006","article-title":"Nongenomic activation of spermatozoa by steroid hormones: facts and fictions","volume":"308","author":"Baldi","year":"2009","journal-title":"Mol. Cell Endocrinol."},{"key":"2023012512182748100_B8","first-page":"415","article-title":"Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements","volume":"5","author":"Butte","year":"2000","journal-title":"Pac. Symp. Biocomput."},{"key":"2023012512182748100_B9","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ccr.2010.10.031","article-title":"AKT inhibition relieves feedback suppression of receptor tyrosine kinase expression and activity","volume":"19","author":"Chandarlapaty","year":"2011","journal-title":"Cancer Cell"},{"key":"2023012512182748100_B10","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/1752-0509-4-16","article-title":"Inferring genetic interactions via a nonlinear model and an optimization algorithm","volume":"4","author":"Chen","year":"2010","journal-title":"BMC Syst. Biol."},{"key":"2023012512182748100_B11","doi-asserted-by":"crossref","DOI":"10.1002\/9781118033340","volume-title":"An Introduction to Optimization","author":"Chong","year":"2008","edition":"3rd"},{"key":"2023012512182748100_B12","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/BF00994110","article-title":"A Bayesian method for the induction of probabilistic networks from data","volume":"9","author":"Cooper","year":"1992","journal-title":"Mach. Learn."},{"key":"2023012512182748100_B13","doi-asserted-by":"crossref","first-page":"e8","DOI":"10.1371\/journal.pbio.0050008","article-title":"Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles","volume":"5","author":"Faith","year":"2007","journal-title":"PLoS Biol."},{"key":"2023012512182748100_B14","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1089\/106652700750050961","article-title":"Using Bayesian networks to analyze expression data","volume":"7","author":"Friedman","year":"2000","journal-title":"J. Comput. Biol."},{"key":"2023012512182748100_B15","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1093\/bioinformatics\/btq131","article-title":"TopoGSA: network topological gene set analysis","volume":"26","author":"Glaab","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012512182748100_B16","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1287\/ijoc.1.3.190","article-title":"Tabu Search - Part I","volume":"1","author":"Glover","year":"1989","journal-title":"ORSA J. Comp."},{"key":"2023012512182748100_B17","first-page":"3","article-title":"Neighborhood size in the simulated annealing algorithm","volume":"8","author":"Goldstein","year":"1988","journal-title":"Am. J. Math. Manag. Sci."},{"key":"2023012512182748100_B18","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1093\/bioinformatics\/btl522","article-title":"Parameter estimation using Simulated Annealing for S-system models of biochemical networks","volume":"23","author":"Gonzalez","year":"2007","journal-title":"Bioinformatics"},{"key":"2023012512182748100_B19","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1287\/moor.13.2.311","article-title":"Cooling schedules for optimal annealing","volume":"13","author":"Hajek","year":"1988","journal-title":"Math. Operat. Res."},{"key":"2023012512182748100_B20","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence.","author":"Holland","year":"1992"},{"key":"2023012512182748100_B21","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1093\/bioinformatics\/btp375","article-title":"Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks","volume":"25","author":"Kaderali","year":"2009","journal-title":"Bioinformatics"},{"key":"2023012512182748100_B22","doi-asserted-by":"crossref","first-page":"D355","DOI":"10.1093\/nar\/gkp896","article-title":"KEGG for representation and analysis of molecular networks involving diseases and drugs","volume":"38","author":"Kanehisa","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"2023012512182748100_B23","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"2023012512182748100_B24","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/bcr1763","article-title":"Clinical trials update: endocrine and biological therapy combinations in the treatment of breast cancer","volume":"9","author":"Leary","year":"2007","journal-title":"Breast Cancer Res."},{"key":"2023012512182748100_B25","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1158\/1078-0432.CCR-09-1764","article-title":"Lapatinib restores hormone sensitivity with differential effects on estrogen receptor signaling in cell models of human epidermal growth factor receptor 2-negative breast cancer with acquired endocrine resistance","volume":"16","author":"Leary","year":"2010","journal-title":"Clin. Cancer Res."},{"key":"2023012512182748100_B26","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1159\/000279388","article-title":"EGFR signaling and drug discovery","volume":"77","author":"Lurje","year":"2009","journal-title":"Oncology"},{"key":"2023012512182748100_B27","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1089\/cmb.2008.09TT","article-title":"Generating realistic in silico gene networks for performance assessment of reverse engineering methods","volume":"16","author":"Marbach","year":"2009","journal-title":"J. Comput. Biol."},{"key":"2023012512182748100_B28","doi-asserted-by":"crossref","first-page":"6286","DOI":"10.1073\/pnas.0913357107","article-title":"Revealing strengths and weaknesses of methods for gene network inference","volume":"107","author":"Marbach","year":"2010","journal-title":"Proc. Natl Acad. Sci. USA"},{"issue":"Suppl. 1","key":"2023012512182748100_B29","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","article-title":"ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context","volume":"7","author":"Margolin","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023012512182748100_B30","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1093\/nar\/gkp481","article-title":"Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies","volume":"37","author":"Medina","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"2023012512182748100_B31","first-page":"79879","article-title":"Information-theoretic inference of large transcriptional regulatory networks","volume":"2007","author":"Meyer","year":"2007","journal-title":"EUROSIP J. Bioinform. Syst. Biol."},{"key":"2023012512182748100_B32","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1186\/1471-2105-9-461","article-title":"minet: A R\/Bioconductor package for inferring large transcriptional networks using mutual information","volume":"9","author":"Meyer","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023012512182748100_B33","volume-title":"Active learning of causal bayes net structure.","author":"Murphy","year":"2001"},{"key":"2023012512182748100_B34","first-page":"331","article-title":"The Bayes net toolbox for MATLAB","volume":"33","author":"Murphy","year":"2001","journal-title":"Comput. Sci. Stat. Proc. Interface"},{"key":"2023012512182748100_B35","first-page":"237","article-title":"EGFR family signaling and its association with breast cancer development and resistance to chemotherapy (Review)","volume":"22","author":"Navolanic","year":"2003","journal-title":"Int. J. Oncol."},{"key":"2023012512182748100_B36","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1158\/1078-0432.CCR-10-1869","article-title":"Gefitinib or placebo in combination with tamoxifen in patients with hormone receptor-positive metastatic breast cancer: a randomized phase II study","volume":"17","author":"Osborne","year":"2011","journal-title":"Clin. Cancer Res."},{"key":"2023012512182748100_B37","doi-asserted-by":"crossref","first-page":"e1001009","DOI":"10.1371\/journal.pcbi.1001009","article-title":"Simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway components","volume":"6","author":"Park","year":"2010","journal-title":"PLoS Comput. Biol."},{"key":"2023012512182748100_B38","doi-asserted-by":"crossref","first-page":"e9202","DOI":"10.1371\/journal.pone.0009202","article-title":"Towards a rigorous assessment of systems biology models: the DREAM3 challenges","volume":"5","author":"Prill","year":"2010","journal-title":"PLoS One"},{"key":"2023012512182748100_B39","doi-asserted-by":"crossref","first-page":"4053","DOI":"10.1109\/TIT.2008.926315","article-title":"Network inference from co-occurrences","volume":"54","author":"Rabbat","year":"2008","journal-title":"IEEE Trans. Inform. Theor."},{"key":"2023012512182748100_B40","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.bbrc.2010.07.092","article-title":"RAF protein-serine\/threonine kinases: structure and regulation","volume":"399","author":"Roskoski","year":"2010","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"2023012512182748100_B41","doi-asserted-by":"crossref","first-page":"331S","DOI":"10.1158\/1078-0432.CCR-031212","article-title":"Cross-talk between estrogen receptor and growth factor pathways as a molecular target for overcoming endocrine resistance","volume":"10","author":"Schiff","year":"2004","journal-title":"Clin. Cancer Res."},{"key":"2023012512182748100_B42","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1038\/ng1165","article-title":"Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data","volume":"34","author":"Segal","year":"2003","journal-title":"Nat. Genet."},{"key":"2023012512182748100_B43","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.1101\/gr.1239303","article-title":"Cytoscape: a software environment for integrated models of biomolecular interaction networks","volume":"13","author":"Shannon","year":"2003","journal-title":"Genome Res."},{"key":"2023012512182748100_B44","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/bioinformatics\/18.2.261","article-title":"Probabilistic Boolean Networks: a rule-based uncertainty model for Gene Regulatory Networks","volume":"18","author":"Shmulevich","year":"2002","journal-title":"Bioinformatics"},{"key":"2023012512182748100_B45","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012512182748100_B46","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/S0960-9822(01)00145-2","article-title":"Activation of the anaphase-promoting complex and degradation of cyclin B is not required for progression from Meiosis I to II in Xenopus oocytes","volume":"11","author":"Taieb","year":"2001","journal-title":"Curr. Biol."},{"key":"2023012512182748100_B47","doi-asserted-by":"crossref","first-page":"13544","DOI":"10.1073\/pnas.0506577102","article-title":"Discovering statistically significant pathways in expression profiling studies","volume":"102","author":"Tian","year":"2005","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012512182748100_B48","doi-asserted-by":"crossref","first-page":"ra20","DOI":"10.1126\/scisignal.2000517","article-title":"Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species","volume":"3","author":"Xu","year":"2010","journal-title":"Sci. Signal."}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/4\/546\/48875146\/bioinformatics_28_4_546.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/4\/546\/48875146\/bioinformatics_28_4_546.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T15:00:12Z","timestamp":1674658812000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/28\/4\/546\/212522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,12,22]]},"references-count":48,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012,2,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btr696","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2012,2,15]]},"published":{"date-parts":[[2011,12,22]]}}}