{"id":"https://openalex.org/W2167174221","doi":"https://doi.org/10.1109/bibe.2008.4696745","title":"Phylogenetic reconstruction with disk-covering and Bayesian approaches","display_name":"Phylogenetic reconstruction with disk-covering and Bayesian approaches","publication_year":2008,"publication_date":"2008-10-01","ids":{"openalex":"https://openalex.org/W2167174221","doi":"https://doi.org/10.1109/bibe.2008.4696745","mag":"2167174221"},"language":"en","primary_location":{"id":"doi:10.1109/bibe.2008.4696745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe.2008.4696745","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 8th IEEE International Conference on BioInformatics and BioEngineering","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/A5101997489","display_name":"Yan Guo","orcid":"https://orcid.org/0000-0002-0886-6621"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yan Guo","raw_affiliation_strings":["Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357202","display_name":"Fei Ye","orcid":"https://orcid.org/0000-0001-6472-5076"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Ye","raw_affiliation_strings":["Cancer Biostatistics Center/Division of Biostatistics, Department of Biostatistics, Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Cancer Biostatistics Center/Division of Biostatistics, Department of Biostatistics, Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001619694","display_name":"Jijun Tang","orcid":"https://orcid.org/0000-0002-6377-536X"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jijun Tang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101997489"],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13074227,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.9962000250816345,"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/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.9962000250816345,"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":0.9951000213623047,"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/T10012","display_name":"Genetic diversity and population structure","score":0.9614999890327454,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6717219352722168},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6054747104644775},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.6008762717247009},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5887503623962402},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5741137862205505},{"id":"https://openalex.org/keywords/divide-and-conquer-algorithms","display_name":"Divide and conquer algorithms","score":0.4656919240951538},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33285677433013916},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28622496128082275}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6717219352722168},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6054747104644775},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.6008762717247009},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5887503623962402},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5741137862205505},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.4656919240951538},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33285677433013916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28622496128082275}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibe.2008.4696745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe.2008.4696745","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 8th IEEE International Conference on BioInformatics and BioEngineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1867732144","https://openalex.org/W2039592185","https://openalex.org/W2041797002","https://openalex.org/W2060425093","https://openalex.org/W2097386339","https://openalex.org/W2097706568","https://openalex.org/W2106228995","https://openalex.org/W2117752525","https://openalex.org/W2128890239","https://openalex.org/W2130416410","https://openalex.org/W2141913814","https://openalex.org/W2146058063","https://openalex.org/W2161444534","https://openalex.org/W2994240441","https://openalex.org/W6639077426","https://openalex.org/W6674647444","https://openalex.org/W6676094112"],"related_works":["https://openalex.org/W71678127","https://openalex.org/W2157655363","https://openalex.org/W4205763938","https://openalex.org/W2292189132","https://openalex.org/W4288092343","https://openalex.org/W4386114318","https://openalex.org/W2134332527","https://openalex.org/W2888496681","https://openalex.org/W2032094637","https://openalex.org/W2790979771"],"abstract_inverted_index":{"The":[0],"DCM":[1],"approach":[2,52,91],"is":[3,53,71,77,93,174],"commonly":[4],"used":[5,55],"to":[6,20,27,106,116,134,143,177,189,195,221],"divide":[7],"the":[8,29,33,39,140,158,163,179,191,196,212,216,223,235,239,243],"dataset":[9,97],"into":[10],"smaller":[11],"subproblems,":[12],"analyze":[13],"each":[14,199],"subproblem":[15,200],"using":[16,168],"a":[17,45,62,89,123,132],"base":[18],"method":[19,43,64,75,133,170,185],"obtain":[21],"subtrees,":[22],"then":[23],"recombine":[24],"these":[25,118],"subtrees":[26],"build":[28],"final":[30],"phylogeny":[31,57,69,172,241],"over":[32,157],"whole":[34,217,240],"dataset.":[35],"In":[36,59,219],"recent":[37],"years,":[38],"new":[40,63,74],"and":[41,92,112,131],"improved":[42,79],"MrBayes,":[44],"Bayesian":[46,68,141,169,184,226],"Markov":[47],"Chain":[48],"Monte":[49],"Carlo":[50],"(MCMC)":[51],"widely":[54],"for":[56,65,95,171,210,215],"analysis.":[58,98],"this":[60],"paper,":[61],"large":[66,96],"scale":[67],"analysis":[70,142],"proposed.":[72],"This":[73],"(DCM3-MrBayes)":[76],"an":[78,231],"version":[80],"of":[81,162,167,225,238],"Rec-I-DCM3":[82],"(recursive":[83],"iterative":[84],"disk-covering":[85],"method),":[86],"which":[87,206],"uses":[88],"divide-and-conquer":[90,183],"designed":[94],"To":[99],"integrate":[100],"MrBayes":[101],"with":[102,108],"Rec-I-DCM3,":[103],"we":[104,228],"have":[105],"deal":[107],"some":[109,208],"unique":[110],"problems":[111],"proposed":[113],"several":[114],"methods":[115],"tackle":[117],"problems.":[119],"Our":[120,149],"improvements":[121],"include":[122],"cache":[124],"system":[125],"that":[126,198,233],"can":[127],"avoid":[128],"unnecessary":[129],"computations":[130],"eliminate":[135],"weak":[136],"branches":[137],"indicated":[138],"by":[139],"filter":[144],"out":[145],"potential":[146],"bad":[147],"branches.":[148],"experiments":[150],"on":[151],"simulated":[152],"datasets":[153],"shows":[154],"promising":[155],"improvement":[156],"original":[159],"DCM.":[160],"One":[161],"most":[164],"important":[165],"advantages":[166],"reconstruction":[173],"being":[175],"able":[176],"calculate":[178,190],"posterior":[180,192,204,213,236,245],"probabilities.":[181,246],"A":[182],"looses":[186],"its":[187,202],"ability":[188],"probabilities":[193,237],"due":[194],"fact":[197],"generates":[201],"own":[203],"probabilities,":[205],"posts":[207],"difficulties":[209],"obtaining":[211],"probability":[214],"problem.":[218],"order":[220],"preserve":[222],"advantage":[224],"approach,":[227],"also":[229],"introduce":[230],"algorithm":[232],"calculates":[234],"from":[242],"subproblemspsila":[244]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
