{"id":"https://openalex.org/W2915925006","doi":"https://doi.org/10.1371/journal.pcbi.1006770","title":"A numerical approach for a discrete Markov model for progressing drug resistance of cancer","display_name":"A numerical approach for a discrete Markov model for progressing drug resistance of cancer","publication_year":2019,"publication_date":"2019-02-19","ids":{"openalex":"https://openalex.org/W2915925006","doi":"https://doi.org/10.1371/journal.pcbi.1006770","mag":"2915925006"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pcbi.1006770","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1006770","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006770&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006770&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085480408","display_name":"Masayuki Maeda","orcid":"https://orcid.org/0000-0002-6268-7106"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Masayuki Maeda","raw_affiliation_strings":["Graduate School of Social Sciences, Tokyo Metropolitan University, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-6268-7106","affiliations":[{"raw_affiliation_string":"Graduate School of Social Sciences, Tokyo Metropolitan University, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113782043","display_name":"Hideaki Yamashita","orcid":null},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideaki Yamashita","raw_affiliation_strings":["Graduate School of Social Sciences, Tokyo Metropolitan University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Social Sciences, Tokyo Metropolitan University, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085480408"],"corresponding_institution_ids":["https://openalex.org/I69740276"],"apc_list":{"value":2655,"currency":"USD","value_usd":2655},"apc_paid":{"value":2655,"currency":"USD","value_usd":2655},"fwci":0.0,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.01845586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"15","issue":"2","first_page":"e1006770","last_page":"e1006770"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10158","display_name":"Cancer Immunotherapy and Biomarkers","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11215","display_name":"Chronic Myeloid Leukemia Treatments","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/2720","display_name":"Hematology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.49211376905441284},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4901965260505676},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.478933185338974},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4728327691555023},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.47232407331466675},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4587562084197998},{"id":"https://openalex.org/keywords/stochastic-process","display_name":"Stochastic process","score":0.45697224140167236},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.39131030440330505},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37140822410583496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34010645747184753},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.16046005487442017},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09464427828788757}],"concepts":[{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.49211376905441284},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4901965260505676},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.478933185338974},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4728327691555023},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.47232407331466675},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4587562084197998},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.45697224140167236},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39131030440330505},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37140822410583496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34010645747184753},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.16046005487442017},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09464427828788757},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1371/journal.pcbi.1006770","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1006770","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006770&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:plo:pcbi00:1006770","is_oa":false,"landing_page_url":"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006770","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:5d2b9d0646db4c36a1ce4da6f23417f2","is_oa":false,"landing_page_url":"https://doaj.org/article/5d2b9d0646db4c36a1ce4da6f23417f2","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS Computational Biology, Vol 15, Iss 2, p e1006770 (2019)","raw_type":"article"},{"id":"pmh:oai:europepmc.org:5395123","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6396936","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"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":null,"raw_type":"Text"},{"id":"pmh:oai:figshare.com:article/7741886","is_oa":true,"landing_page_url":"https://figshare.com/articles/dataset/A_numerical_approach_for_a_discrete_Markov_model_for_progressing_drug_resistance_of_cancer/7741886","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":"","raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.1371/journal.pcbi.1006770","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1006770","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006770&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8700000047683716}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2915925006.pdf","grobid_xml":"https://content.openalex.org/works/W2915925006.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2020299413","https://openalex.org/W2058564254","https://openalex.org/W2066330900","https://openalex.org/W2077393203","https://openalex.org/W2082580993","https://openalex.org/W2090004346","https://openalex.org/W2098560585","https://openalex.org/W2140544199","https://openalex.org/W2146489738","https://openalex.org/W2154856176","https://openalex.org/W2155582899","https://openalex.org/W2173690212","https://openalex.org/W2179429273"],"related_works":["https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2413828414","https://openalex.org/W2367222340","https://openalex.org/W1567439243","https://openalex.org/W1993731165","https://openalex.org/W2095860056","https://openalex.org/W2742871814","https://openalex.org/W2802778578"],"abstract_inverted_index":{"The":[0,56,175,263,282,306],"presence":[1],"of":[2,13,19,26,41,52,58,70,77,94,100,106,119,124,142,148,161,164,178,187,194,213,216,233,244,260,265,271,288,304,309,318,336],"treatment-resistant":[3],"cells":[4,45,72,96,120,126,144],"is":[5,21,61,298,312],"an":[6],"important":[7,299],"factor":[8],"that":[9,285],"limits":[10],"the":[11,17,23,27,39,50,68,74,92,98,104,116,122,140,146,151,159,192,211,219,230,237,241,286,291,295,302,315,334,337],"efficacy":[12],"cancer":[14,44],"therapy,":[15],"and":[16,46,73,83,97,121,132,145,171,202,227,240,248,323],"prospect":[18],"resistance":[20,149,272,319],"considered":[22],"major":[24],"cause":[25],"treatment":[28],"strategy.":[29],"Several":[30],"recent":[31],"studies":[32],"have":[33],"employed":[34],"mathematical":[35],"models":[36],"to":[37,48,62,66,87,138,185,209,254,313],"elucidate":[38],"dynamics":[40],"generating":[42],"resistant":[43,54,71,95,125,143],"attempted":[47],"predict":[49],"probability":[51,76,99,147,303],"emerging":[53,75,269,301,316],"cells.":[55],"purpose":[57],"this":[59],"paper":[60],"present":[63],"numerical":[64,176,231,310],"approach":[65,311],"compute":[67,91,255,314],"number":[69,93,118,123,141,287],"resistance.":[78,101,305],"Stochastic":[79],"model":[80,103],"was":[81,127,207,222],"designed":[82],"developed":[84],"a":[85,108,257],"method":[86,181,250],"approximately":[88],"but":[89],"efficiently":[90],"To":[102,157],"progression":[105],"cancer,":[107],"discrete-state,":[109],"two-dimensional":[110],"Markov":[111],"process":[112],"whose":[113],"states":[114],"are":[115],"total":[117],"employed.":[128],"Then":[129,204],"exact":[130,169,188],"analysis":[131,170,189,335],"approximate":[133],"aggregation":[134],"approaches":[135],"were":[136,173,182,273],"proposed":[137],"calculate":[139],"when":[150,218],"cell":[152,261,292],"population":[153,293],"reaches":[154],"detection":[155,196,220,296],"size.":[156,262],"confirm":[158,210],"accuracy":[160,212],"computed":[162,214,277],"results":[163,215,232,283],"approximation,":[165],"relative":[166],"errors":[167],"between":[168,236],"approximation":[172,180,217,234,280,329],"computed.":[174],"values":[177,278],"our":[179,249],"very":[183],"close":[184],"those":[186],"calculated":[190],"in":[191],"range":[193,259],"small":[195],"size":[197,221,297],"M":[198,223],"=":[199,224],"500,":[200],"100,":[201],"1500.":[203],"computer":[205],"simulation":[206],"performed":[208],"10000,30000,50000,":[225],"100000":[226],"1000000.":[228],"All":[229],"fell":[235],"upper":[238],"level":[239,243],"lower":[242],"95%":[245],"confidential":[246],"intervals":[247],"took":[251],"less":[252],"time":[253],"over":[256],"broad":[258],"effects":[264],"parameter":[266],"change":[267],"on":[268],"probabilities":[270,317],"also":[274],"investigated":[275],"by":[276],"using":[279],"method.":[281],"showed":[284],"divisions":[289],"until":[290],"reached":[294],"for":[300,333],"next":[307],"step":[308],"under":[320],"drug":[321],"administration":[322],"with":[324],"multiple":[325],"mutation.":[326],"Another":[327],"effective":[328],"would":[330],"be":[331],"necessary":[332],"latter":[338],"case.":[339]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}