{"id":"https://openalex.org/W2158698461","doi":"https://doi.org/10.1002/sam.10085","title":"A Machine-Learning Approach to Detecting Unknown Bacterial Serovars.","display_name":"A Machine-Learning Approach to Detecting Unknown Bacterial Serovars.","publication_year":2010,"publication_date":"2010-10-01","ids":{"openalex":"https://openalex.org/W2158698461","doi":"https://doi.org/10.1002/sam.10085","mag":"2158698461","pmid":"https://pubmed.ncbi.nlm.nih.gov/22162745"},"language":"en","primary_location":{"id":"pmid:22162745","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/22162745","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":"Statistical analysis and data mining","raw_type":null},"type":"article","indexed_in":["pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ferit Akova","orcid":null},"institutions":[{"id":"https://openalex.org/I55769427","display_name":"Indiana University \u2013 Purdue University Indianapolis","ror":"https://ror.org/05gxnyn08","country_code":"US","type":"education","lineage":["https://openalex.org/I55769427","https://openalex.org/I592451"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ferit Akova","raw_affiliation_strings":["Department of Computer and Information Science, Indiana University-Purdue University, Indianapolis, IN 46202, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, Indiana University-Purdue University, Indianapolis, IN 46202, USA","institution_ids":["https://openalex.org/I55769427"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Murat Dundar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Murat Dundar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"V Jo Davisson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V Jo Davisson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"E Daniel Hirleman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"E Daniel Hirleman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Arun K Bhunia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arun K Bhunia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"J Paul Robinson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J Paul Robinson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Bartek Rajwa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bartek Rajwa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.1163309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"3","issue":"5","first_page":"289","last_page":"301"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.32350000739097595,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.32350000739097595,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/T12167","display_name":"Bacterial Identification and Susceptibility Testing","score":0.2833000123500824,"subfield":{"id":"https://openalex.org/subfields/1308","display_name":"Clinical Biochemistry"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.06689999997615814,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5627999901771545},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5217000246047974},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4909999966621399},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4478999972343445},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.44449999928474426},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4431000053882599},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.43619999289512634},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4178999960422516}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7046999931335449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6254000067710876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5662999749183655},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5627999901771545},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5217000246047974},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4478999972343445},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.44449999928474426},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4431000053882599},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.43619999289512634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38119998574256897},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3147999942302704},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2824000120162964},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C26004113","wikidata":"https://www.wikidata.org/wiki/Q3711784","display_name":"Conjugate prior","level":4,"score":0.26269999146461487},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"pmid:22162745","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/22162745","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":"Statistical analysis and data mining","raw_type":null},{"id":"pmh:oai:europepmc.org:2239513","is_oa":false,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3230886","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3884094910","display_name":null,"funder_award_id":"R21 AI085531","funder_id":"https://openalex.org/F4320337355","funder_display_name":"National Institute of Allergy and Infectious Diseases"}],"funders":[{"id":"https://openalex.org/F4320337355","display_name":"National Institute of Allergy and Infectious Diseases","ror":"https://ror.org/043z4tv69"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2184236393","https://openalex.org/W4292196801"],"related_works":[],"abstract_inverted_index":{"Technologies":[0],"for":[1,9,19,29,87,108,117,199,220],"rapid":[2],"detection":[3,110],"of":[4,22,48,60,62,70,79,111,129,168,186],"bacterial":[5,49,72,113],"pathogens":[6],"are":[7],"crucial":[8],"securing":[10],"the":[11,51,67,77,123,133,146,174,187,214],"food":[12],"supply.":[13],"A":[14],"light-scattering":[15],"sensor":[16],"recently":[17,242],"developed":[18],"real-time":[20],"identification":[21],"multiple":[23],"colonies":[24],"has":[25],"shown":[26],"great":[27],"promise":[28],"distinguishing":[30],"bacteria":[31,209],"cultures.":[32],"The":[33,126,223],"classification":[34,47],"approach":[35,100,205,225,245],"currently":[36],"used":[37],"with":[38,103,177,195],"this":[39,94,162],"system":[40],"relies":[41],"on":[42,206,213,247],"supervised":[43],"learning.":[44],"For":[45],"accurate":[46],"pathogens,":[50],"training":[52,106,124,175],"library":[53],"should":[54,58],"be":[55],"exhaustive,":[56],"i.e.,":[57,115],"consist":[59],"samples":[61,120],"all":[63],"possible":[64],"pathogens.":[65],"Yet,":[66],"sheer":[68],"number":[69],"existing":[71],"serovars":[73,116],"and":[74,90,171,211,233],"more":[75,184],"importantly":[76],"effect":[78],"their":[80],"high":[81],"mutation":[82],"rate":[83],"would":[84],"not":[85],"allow":[86],"a":[88,98,104,193,207,241],"practical":[89],"manageable":[91],"training.":[92],"In":[93],"study,":[95],"we":[96,164],"propose":[97],"Bayesian":[99,244],"to":[101,144,153,180],"learning":[102],"nonexhaustive":[105],"dataset":[107,176,210,219],"automated":[109],"unmatched":[112],"serovars,":[114],"which":[118],"no":[119],"exist":[121],"in":[122,192],"library.":[125],"main":[127],"contribution":[128],"our":[130,204],"work":[131],"is":[132,226],"Wishart":[134],"conjugate":[135],"priors":[136],"defined":[137],"over":[138],"class":[139],"distributions.":[140],"This":[141,190],"allows":[142],"us":[143],"employ":[145],"prior":[147],"information":[148],"obtained":[149],"from":[150],"known":[151],"classes":[152,158,167,179],"make":[154,181],"inferences":[155],"about":[156],"unknown":[157],"as":[159,238,240],"well.":[160],"By":[161],"means,":[163],"identify":[165],"new":[166],"informational":[169],"value":[170],"dynamically":[172],"update":[173],"these":[178],"it":[182],"increasingly":[183],"representative":[185],"sample":[188],"population.":[189],"results":[191],"classifier":[194],"improved":[196],"predictive":[197],"performance":[198],"future":[200],"samples.":[201],"We":[202],"evaluated":[203],"28-class":[208],"also":[212],"benchmark":[215],"26-class":[216],"letter":[217],"recognition":[218],"further":[221],"validation.":[222],"proposed":[224],"compared":[227],"against":[228],"state-of-the-art":[229],"involving":[230],"density-based":[231],"approaches":[232],"support":[234],"vector":[235],"domain":[236],"description,":[237],"well":[239],"introduced":[243],"based":[246],"simulated":[248],"classes.":[249]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-06-24T00:00:00"}