{"id":"https://openalex.org/W2059509822","doi":"https://doi.org/10.1186/1471-2105-10-352","title":"Bayesian modeling of ChIP-chip data using latent variables","display_name":"Bayesian modeling of ChIP-chip data using latent variables","publication_year":2009,"publication_date":"2009-10-26","ids":{"openalex":"https://openalex.org/W2059509822","doi":"https://doi.org/10.1186/1471-2105-10-352","mag":"2059509822","pmid":"https://pubmed.ncbi.nlm.nih.gov/19857265"},"language":"en","primary_location":{"id":"doi:10.1186/1471-2105-10-352","is_oa":true,"landing_page_url":"https://doi.org/10.1186/1471-2105-10-352","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-10-352","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-10-352","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078336864","display_name":"Mingqi Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingqi Wu","raw_affiliation_strings":["Department of Statistics, Texas A&M University, College Station, TX 77843, USA. mqwu@stat.tamu.edu","Department of Statistics, Texas A&M University, College Station, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Texas A&M University, College Station, TX 77843, USA. mqwu@stat.tamu.edu","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"Department of Statistics, Texas A&M University, College Station, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085287370","display_name":"Faming Liang","orcid":"https://orcid.org/0000-0002-1177-5501"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Faming Liang","raw_affiliation_strings":["Department of Statistics, Texas A&M University, College Station, TX, 77843, USA","Department of Statistics, Texas A&M University, College Station, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Texas A&M University, College Station, TX, 77843, USA","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"Department of Statistics, Texas A&M University, College Station, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101751310","display_name":"Yanan Tian","orcid":"https://orcid.org/0000-0002-9946-2998"},"institutions":[{"id":"https://openalex.org/I2801613365","display_name":"Mitchell Institute","ror":"https://ror.org/03ds72003","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2801613365"]},{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanan Tian","raw_affiliation_strings":["Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, 77843, USA","Texas A&M University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, 77843, USA","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"Texas A&M University","institution_ids":["https://openalex.org/I2801613365","https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1690,"currency":"GBP","value_usd":2072},"apc_paid":{"value":1690,"currency":"GBP","value_usd":2072},"fwci":0.2739,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60224866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":"1","first_page":"352","last_page":"352"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.3212999999523163,"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/T10885","display_name":"Gene expression and cancer classification","score":0.3212999999523163,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.25029999017715454,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.03020000085234642,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6429234743118286},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6365891695022583},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5459075570106506},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.47185656428337097},{"id":"https://openalex.org/keywords/bayesian-hierarchical-modeling","display_name":"Bayesian hierarchical modeling","score":0.4620470404624939},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4440787732601166},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4185653328895569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3837549686431885},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3692333698272705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3299436867237091}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6429234743118286},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6365891695022583},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5459075570106506},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.47185656428337097},{"id":"https://openalex.org/C191413810","wikidata":"https://www.wikidata.org/wiki/Q17100952","display_name":"Bayesian hierarchical modeling","level":4,"score":0.4620470404624939},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4440787732601166},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4185653328895569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3837549686431885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3692333698272705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3299436867237091}],"mesh":[{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016012","descriptor_name":"Poisson Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016012","descriptor_name":"Poisson Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016012","descriptor_name":"Poisson Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D020869","descriptor_name":"Gene Expression Profiling","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D020869","descriptor_name":"Gene Expression Profiling","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D020869","descriptor_name":"Gene Expression Profiling","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D047369","descriptor_name":"Chromatin Immunoprecipitation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D047369","descriptor_name":"Chromatin Immunoprecipitation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D047369","descriptor_name":"Chromatin Immunoprecipitation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":6,"locations":[{"id":"doi:10.1186/1471-2105-10-352","is_oa":true,"landing_page_url":"https://doi.org/10.1186/1471-2105-10-352","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-10-352","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},{"id":"pmid:19857265","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/19857265","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC bioinformatics","raw_type":null},{"id":"pmh:oai:doaj.org/article:778e9ff12a56473cba6764c25f95a744","is_oa":true,"landing_page_url":"https://doaj.org/article/778e9ff12a56473cba6764c25f95a744","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics, Vol 10, Iss 1, p 352 (2009)","raw_type":"article"},{"id":"pmh:oai:oaktrust.library.tamu.edu:1969.1/179731","is_oa":false,"landing_page_url":"https://hdl.handle.net/1969.1/179731","pdf_url":null,"source":{"id":"https://openalex.org/S4306400291","display_name":"OakTrust (Texas A&M University Libraries)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I91045830","host_organization_name":"Texas A&M University","host_organization_lineage":["https://openalex.org/I91045830"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:pubmedcentral.nih.gov:2779819","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2779819","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics","raw_type":"Text"},{"id":"pmh:oai:repository.kaust.edu.sa:10754/596770","is_oa":true,"landing_page_url":"http://hdl.handle.net/10754/596770","pdf_url":null,"source":{"id":"https://openalex.org/S4306401596","display_name":"King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I71920554","host_organization_name":"King Abdullah University of Science and Technology","host_organization_lineage":["https://openalex.org/I71920554"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1186/1471-2105-10-352","is_oa":true,"landing_page_url":"https://doi.org/10.1186/1471-2105-10-352","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-10-352","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1682494006","display_name":null,"funder_award_id":"KUS-C1-016-04","funder_id":"https://openalex.org/F4320322320","funder_display_name":"King Abdullah University of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320322320","display_name":"King Abdullah University of Science and Technology","ror":"https://ror.org/01q3tbs38"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2059509822.pdf","grobid_xml":"https://content.openalex.org/works/W2059509822.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1498486940","https://openalex.org/W1824047490","https://openalex.org/W1876857396","https://openalex.org/W1993304320","https://openalex.org/W2007684630","https://openalex.org/W2009860000","https://openalex.org/W2020634541","https://openalex.org/W2025040226","https://openalex.org/W2033403400","https://openalex.org/W2052301225","https://openalex.org/W2053833990","https://openalex.org/W2059979506","https://openalex.org/W2068193616","https://openalex.org/W2076889248","https://openalex.org/W2079026635","https://openalex.org/W2088995337","https://openalex.org/W2093023711","https://openalex.org/W2097705100","https://openalex.org/W2106554405","https://openalex.org/W2120865735","https://openalex.org/W2125838338","https://openalex.org/W2128860595","https://openalex.org/W2130416410","https://openalex.org/W2132508108","https://openalex.org/W2140022443","https://openalex.org/W2148534890","https://openalex.org/W2155653793","https://openalex.org/W2156933633","https://openalex.org/W2164549386","https://openalex.org/W2168390964","https://openalex.org/W3008420551","https://openalex.org/W3099817059","https://openalex.org/W3143625801","https://openalex.org/W4235169531","https://openalex.org/W4251644969"],"related_works":["https://openalex.org/W4283770175","https://openalex.org/W3168675052","https://openalex.org/W3087071515","https://openalex.org/W4244648127","https://openalex.org/W2793406240","https://openalex.org/W4283077537","https://openalex.org/W2999603699","https://openalex.org/W2464065341","https://openalex.org/W2947536360","https://openalex.org/W3086697448"],"abstract_inverted_index":{"BACKGROUND:":[0],"The":[1,136,259,289],"ChIP-chip":[2,47,134],"technology":[3],"has":[4],"been":[5,39],"used":[6],"in":[7,32,41,162],"a":[8,128,183,200,235],"wide":[9],"range":[10],"of":[11,17,24,29,69,71,88,119,182,208,212,231,276],"biomedical":[12],"studies,":[13],"such":[14,49,146],"as":[15,50,147],"identification":[16],"human":[18],"transcription":[19],"factor":[20],"binding":[21],"sites,":[22],"investigation":[23,28],"DNA":[25],"methylation,":[26],"and":[27,34,60,74,112,157,175,194,215,268,285],"histone":[30],"modifications":[31],"animals":[33],"plants.":[35],"Various":[36],"methods":[37,78,93],"have":[38],"proposed":[40],"the":[42,46,51,55,66,72,84,90,110,133,142,148,152,158,169,172,180,187,191,195,209,213,218,223,228,247,252,277,294,304],"literature":[43],"for":[44,132,186,246],"analyzing":[45],"data,":[48,188],"sliding":[52,286],"window":[53,287],"methods,":[54,59,89,280,284,301],"hidden":[56,281],"Markov":[57,282],"model-based":[58],"Bayesian":[61,77,92,129,144,159,260,279,295],"methods.":[62,288],"Although,":[63],"due":[64,116],"to":[65,108,117,222,266],"integrated":[67],"consideration":[68],"uncertainty":[70],"models":[73,227],"model":[75,131,138,185,219,283],"parameters,":[76],"can":[79,298],"potentially":[80],"work":[81],"better":[82],"than":[83],"other":[85,300],"two":[86,163],"classes":[87],"existing":[91,143,278],"do":[94],"not":[95],"perform":[96],"satisfactorily.":[97],"They":[98],"usually":[99],"require":[100],"multiple":[101],"replicates":[102],"or":[103],"some":[104,275],"extra":[105],"experimental":[106],"information":[107],"parametrize":[109],"model,":[111,151,156,161,214],"long":[113],"CPU":[114],"time":[115],"involving":[118],"MCMC":[120,206],"simulations.":[121],"RESULTS:":[122],"In":[123],"this":[124,202],"paper,":[125],"we":[126],"propose":[127],"latent":[130,236,248,261,296],"data.":[135],"new":[137],"mainly":[139],"differs":[140],"from":[141],"models,":[145],"joint":[149],"deconvolution":[150],"hierarchical":[153,160],"gamma":[154],"mixture":[155],"respects.":[164],"Firstly,":[165],"it":[166,226],"works":[167],"on":[168],"difference":[170],"between":[171],"averaged":[173],"treatment":[174],"control":[176],"samples.":[177],"This":[178],"enables":[179,203],"use":[181],"simple":[184],"which":[189],"avoids":[190],"probe-specific":[192],"effect":[193],"sample":[196],"(control/treatment)":[197],"effect.":[198],"As":[199],"consequence,":[201],"an":[204],"efficient":[205],"simulation":[207],"posterior":[210],"distribution":[211,243],"also":[216],"makes":[217],"more":[220],"robust":[221],"outliers.":[224,307],"Secondly,":[225],"neighboring":[229],"dependence":[230],"probes":[232],"by":[233],"introducing":[234],"indicator":[237,249],"vector.":[238],"A":[239],"truncated":[240],"Poisson":[241],"prior":[242],"is":[244,263],"assumed":[245],"variable,":[250],"with":[251,272,274],"rationale":[253],"being":[254],"justified":[255],"at":[256],"length.":[257],"CONCLUSION:":[258],"method":[262,297],"successfully":[264],"applied":[265],"real":[267],"ten":[269],"simulated":[270],"datasets,":[271],"comparisons":[273],"numerical":[290],"results":[291],"indicate":[292],"that":[293],"outperform":[299],"especially":[302],"when":[303],"data":[305],"contain":[306]},"counts_by_year":[{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
