{"id":"https://openalex.org/W2113118632","doi":"https://doi.org/10.1109/cibcb.2010.5510433","title":"A regression tree-based Gibbs sampler to learn the regulation programs in a transcription regulatory module network","display_name":"A regression tree-based Gibbs sampler to learn the regulation programs in a transcription regulatory module network","publication_year":2010,"publication_date":"2010-05-01","ids":{"openalex":"https://openalex.org/W2113118632","doi":"https://doi.org/10.1109/cibcb.2010.5510433","mag":"2113118632"},"language":"en","primary_location":{"id":"doi:10.1109/cibcb.2010.5510433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cibcb.2010.5510433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021106844","display_name":"Jianlong Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Jianlong Qi","raw_affiliation_strings":["Department of Computer Science, Concordia University, Montreal, Canada","Department of Computer Science Concordia University, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Computer Science Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061259711","display_name":"Tom Michoel","orcid":"https://orcid.org/0000-0003-4749-4725"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]},{"id":"https://openalex.org/I12607205","display_name":"University College Ghent","ror":"https://ror.org/00rs45z86","country_code":"BE","type":"education","lineage":["https://openalex.org/I12607205"]},{"id":"https://openalex.org/I4210098178","display_name":"VIB-UGent Center for Plant Systems Biology","ror":"https://ror.org/01qnqmc89","country_code":"BE","type":"facility","lineage":["https://openalex.org/I2802017950","https://openalex.org/I32597200","https://openalex.org/I4210098178"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Tom Michoel","raw_affiliation_strings":["Department of Plant Systems Biology, VIB, Department of Plant Biotechnology and Genetics, Ghent University College, Ghent, Belgium","Department of Plant Systems Biology, VIB, Department of Plant Biotechnology and Genetics, Ghent University, Technologiepark 927, B-9052 Ghent, Belgium"],"affiliations":[{"raw_affiliation_string":"Department of Plant Systems Biology, VIB, Department of Plant Biotechnology and Genetics, Ghent University College, Ghent, Belgium","institution_ids":["https://openalex.org/I4210098178","https://openalex.org/I32597200","https://openalex.org/I12607205"]},{"raw_affiliation_string":"Department of Plant Systems Biology, VIB, Department of Plant Biotechnology and Genetics, Ghent University, Technologiepark 927, B-9052 Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041471776","display_name":"Gregory Butler","orcid":"https://orcid.org/0000-0002-6938-0879"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Gregory Butler","raw_affiliation_strings":["Department of Computer Science, Concordia University, Montreal, Canada","Department of Computer Science Concordia University, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Computer Science Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021106844"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":1.0574,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.75848467,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"39","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9700999855995178,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7706374526023865},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6747201681137085},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6358122825622559},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.586764931678772},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.569831371307373},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47617214918136597},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4682038426399231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3897479474544525},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.33576035499572754},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23562654852867126},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12427279353141785}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7706374526023865},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6747201681137085},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6358122825622559},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.586764931678772},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.569831371307373},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47617214918136597},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4682038426399231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3897479474544525},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.33576035499572754},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23562654852867126},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12427279353141785},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cibcb.2010.5510433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cibcb.2010.5510433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1615454278","https://openalex.org/W1851422093","https://openalex.org/W1970679073","https://openalex.org/W1981727151","https://openalex.org/W2025803032","https://openalex.org/W2044525257","https://openalex.org/W2049633694","https://openalex.org/W2069739265","https://openalex.org/W2104877015","https://openalex.org/W2125631472","https://openalex.org/W2137683543","https://openalex.org/W2140665226","https://openalex.org/W2150926065","https://openalex.org/W2154354945","https://openalex.org/W2154664365","https://openalex.org/W2161986541","https://openalex.org/W2162891889","https://openalex.org/W2167190345","https://openalex.org/W2330192890","https://openalex.org/W2611370172","https://openalex.org/W3104533353","https://openalex.org/W4211064163","https://openalex.org/W6642967724","https://openalex.org/W6676167818"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W840335986","https://openalex.org/W1501929113"],"abstract_inverted_index":{"Many":[0],"algorithms":[1],"have":[2,16],"been":[3],"proposed":[4],"to":[5,82,110,165],"learn":[6],"transcription":[7],"regulatory":[8],"networks":[9,15],"from":[10,150],"gene":[11],"expression":[12],"data.":[13,193],"Bayesian":[14,85],"obtained":[17],"promising":[18],"results,":[19],"in":[20,28,128,173,187],"particular,":[21],"the":[22,48,52,84,91,113,158,185],"module":[23,30,66,129],"network":[24],"method.":[25],"The":[26,61,87,131,177],"genes":[27],"a":[29,32,39,65,71,77,118,138,151,167],"share":[31],"regulation":[33,62,106,126],"program":[34,63],"(regression":[35],"tree),":[36],"consisting":[37],"of":[38,41,55,64,79,90,103,133,140,160,179],"set":[40,139,159],"parents":[42],"and":[43,57,154,190],"conditional":[44],"probability":[45],"distributions.":[46],"Hence,":[47],"method":[49],"significantly":[50],"decreases":[51],"search":[53,73,93],"space":[54],"models":[56],"consequently":[58],"avoids":[59],"overfitting.":[60],"is":[67,95,136,143,163,182],"normally":[68],"learned":[69],"by":[70,184],"deterministic":[72,92],"algorithm,":[74],"which":[75],"performs":[76],"series":[78],"greedy":[80],"operations":[81,142,162],"maximize":[83],"score.":[86],"major":[88],"shortcoming":[89],"algorithm":[94,123,181],"that":[96,137,157],"its":[97],"result":[98],"may":[99],"only":[100],"represent":[101],"one":[102],"several":[104],"possible":[105],"programs.":[107],"In":[108],"order":[109],"account":[111],"for":[112,124,145],"model":[114],"uncertainty,":[115],"we":[116,155],"propose":[117],"regression":[119,148],"tree-based":[120],"Gibbs":[121,170],"sampling":[122],"learning":[125],"programs":[127],"networks.":[130],"novelty":[132],"this":[134],"work":[135],"tree":[141,153,161],"defined":[144],"generating":[146],"new":[147],"trees":[149],"given":[152],"show":[156],"sufficient":[164],"generate":[166],"well":[168],"mixing":[169],"sampler":[171],"even":[172],"large":[174],"data":[175,189],"sets.":[176],"effectiveness":[178],"our":[180],"demonstrated":[183],"experiments":[186],"synthetic":[188],"real":[191],"biological":[192]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}