{"id":"https://openalex.org/W4283260535","doi":"https://doi.org/10.1371/journal.pcbi.1010191","title":"Efficient Bayesian inference for mechanistic modelling with high-throughput data","display_name":"Efficient Bayesian inference for mechanistic modelling with high-throughput data","publication_year":2022,"publication_date":"2022-06-21","ids":{"openalex":"https://openalex.org/W4283260535","doi":"https://doi.org/10.1371/journal.pcbi.1010191","pmid":"https://pubmed.ncbi.nlm.nih.gov/35727839"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pcbi.1010191","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010191","pdf_url":null,"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","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1371/journal.pcbi.1010191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078103400","display_name":"Simon F. Martina-Perez","orcid":"https://orcid.org/0000-0001-8596-8595"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Simon Martina Perez","raw_affiliation_strings":["Mathematical Institute, University of Oxford, Oxford, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-8596-8595","affiliations":[{"raw_affiliation_string":"Mathematical Institute, University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068600717","display_name":"Heba Sailem","orcid":"https://orcid.org/0000-0002-6600-1255"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Heba Sailem","raw_affiliation_strings":["Institute of Biomedical Engineering Science, University of Oxford, Oxford, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-6600-1255","affiliations":[{"raw_affiliation_string":"Institute of Biomedical Engineering Science, University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047996039","display_name":"Ruth E. Baker","orcid":"https://orcid.org/0000-0002-6304-9333"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ruth E. Baker","raw_affiliation_strings":["Mathematical Institute, University of Oxford, Oxford, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-6304-9333","affiliations":[{"raw_affiliation_string":"Mathematical Institute, University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078103400"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":{"value":2655,"currency":"USD","value_usd":2655},"apc_paid":{"value":2655,"currency":"USD","value_usd":2655},"fwci":1.676,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.85510259,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"18","issue":"6","first_page":"e1010191","last_page":"e1010191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10604","display_name":"RNA Research and Splicing","score":0.9932000041007996,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9930999875068665,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.8120521306991577},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.724185585975647},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6891850829124451},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6116495132446289},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5932554006576538},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5902884602546692},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5168333649635315},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45554038882255554},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3991902768611908},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35522013902664185},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3265995979309082}],"concepts":[{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.8120521306991577},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.724185585975647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6891850829124451},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.6116495132446289},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5932554006576538},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5902884602546692},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5168333649635315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45554038882255554},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3991902768611908},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35522013902664185},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3265995979309082},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"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":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.1371/journal.pcbi.1010191","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010191","pdf_url":null,"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":"pmid:35727839","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35727839","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":"PLoS computational biology","raw_type":null},{"id":"pmh:oai:RePEc:plo:pcbi00:1010191","is_oa":false,"landing_page_url":"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010191","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:b0267a943774430db8a24803137e3fc1","is_oa":true,"landing_page_url":"https://doaj.org/article/b0267a943774430db8a24803137e3fc1","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS Computational Biology, Vol 18, Iss 6, p e1010191 (2022)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:9249175","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9249175","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":"PLoS Comput Biol","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1371/journal.pcbi.1010191","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010191","pdf_url":null,"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":[],"awards":[{"id":"https://openalex.org/G6215818193","display_name":null,"funder_award_id":"204724/Z/16/Z","funder_id":"https://openalex.org/F4320307874","funder_display_name":"Wellcome"},{"id":"https://openalex.org/G920613653","display_name":null,"funder_award_id":"204724/Z/16/Z","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320307874","display_name":"Wellcome","ror":"https://ror.org/029chgv08"},{"id":"https://openalex.org/F4320314731","display_name":"UK Research and Innovation","ror":"https://ror.org/001aqnf71"},{"id":"https://openalex.org/F4320320006","display_name":"Royal Society","ror":"https://ror.org/03wnrjx87"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1553998446","https://openalex.org/W1965555277","https://openalex.org/W1996881001","https://openalex.org/W2000186700","https://openalex.org/W2032616735","https://openalex.org/W2035967549","https://openalex.org/W2042769067","https://openalex.org/W2081561687","https://openalex.org/W2085315567","https://openalex.org/W2095685494","https://openalex.org/W2147357149","https://openalex.org/W2151729750","https://openalex.org/W2152246075","https://openalex.org/W2155418451","https://openalex.org/W2169349514","https://openalex.org/W2536652083","https://openalex.org/W2593960688","https://openalex.org/W2608310759","https://openalex.org/W2768497893","https://openalex.org/W2799042347","https://openalex.org/W2895152177","https://openalex.org/W2901394719","https://openalex.org/W2913715341","https://openalex.org/W2962857826","https://openalex.org/W2963977539","https://openalex.org/W2994659735","https://openalex.org/W3000269992","https://openalex.org/W3009943122","https://openalex.org/W3010485695","https://openalex.org/W3017711825","https://openalex.org/W3033895182","https://openalex.org/W3045497015","https://openalex.org/W3081814301","https://openalex.org/W3106513449","https://openalex.org/W3204234525","https://openalex.org/W3209895250","https://openalex.org/W4211177544","https://openalex.org/W4225552323","https://openalex.org/W4234117503","https://openalex.org/W4246544788","https://openalex.org/W4297736277","https://openalex.org/W6810354084"],"related_works":["https://openalex.org/W2372267530","https://openalex.org/W4297145487","https://openalex.org/W3012988968","https://openalex.org/W4287824571","https://openalex.org/W2969189870","https://openalex.org/W3015855446","https://openalex.org/W4303857162","https://openalex.org/W2966726156","https://openalex.org/W3113268434","https://openalex.org/W2965643117"],"abstract_inverted_index":{"Bayesian":[0,24,71,102],"methods":[1],"are":[2],"routinely":[3],"used":[4],"to":[5,13,40,69,118,153],"combine":[6],"experimental":[7,154],"data":[8,37,48,120],"with":[9,26,49],"detailed":[10,27,108],"mathematical":[11,109],"models":[12,28],"obtain":[14],"insights":[15],"into":[16],"physical":[17],"phenomena.":[18],"However,":[19],"the":[20,58,96,172],"computational":[21,97],"cost":[22,98],"of":[23,47,60,77,95,99,111,122,130,139,156,174],"computation":[25],"has":[29],"been":[30],"a":[31,66,74,78,93,100,107,119,168],"notorious":[32],"problem.":[33],"Moreover,":[34],"while":[35],"high-throughput":[36,79],"presents":[38],"opportunities":[39],"calibrate":[41],"sophisticated":[42],"models,":[43],"comparing":[44,150],"large":[45],"amounts":[46],"model":[50,110],"simulations":[51],"quickly":[52],"becomes":[53],"computationally":[54],"prohibitive.":[55],"Inspired":[56],"by":[57],"method":[59],"Stochastic":[61],"Gradient":[62],"Descent,":[63],"we":[64,84,126,162],"propose":[65],"minibatch":[67],"approach":[68],"approximate":[70],"computation.":[72],"Through":[73],"case":[75],"study":[76],"imaging":[80],"scratch":[81],"assay":[82],"experiment,":[83],"show":[85],"that":[86,164],"reliable":[87],"inference":[88,103],"can":[89],"be":[90],"performed":[91],"at":[92],"fraction":[94],"traditional":[101],"scheme.":[104],"By":[105,149],"applying":[106],"single":[112],"cell":[113,141,157],"motility,":[114],"proliferation":[115,147],"and":[116,143,146,159],"death":[117],"set":[121],"118":[123],"gene":[124,131],"knockdowns,":[125,132],"characterise":[127],"functional":[128],"subgroups":[129],"each":[133],"displaying":[134],"its":[135],"own":[136],"typical":[137],"combination":[138],"local":[140],"density-dependent":[142,165],"-independent":[144],"motility":[145],"patterns.":[148],"these":[151],"patterns":[152],"measurements":[155],"counts":[158],"wound":[160,175],"closure,":[161],"find":[163],"interactions":[166],"play":[167],"crucial":[169],"role":[170],"in":[171],"process":[173],"healing.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2022-06-23T00:00:00"}
