{"id":"https://openalex.org/W2049676713","doi":"https://doi.org/10.1145/1854776.1854813","title":"Semi-supervised sequence classification using abstraction augmented Markov models","display_name":"Semi-supervised sequence classification using abstraction augmented Markov models","publication_year":2010,"publication_date":"2010-08-02","ids":{"openalex":"https://openalex.org/W2049676713","doi":"https://doi.org/10.1145/1854776.1854813","mag":"2049676713"},"language":"en","primary_location":{"id":"doi:10.1145/1854776.1854813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1854776.1854813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology","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/A5089085275","display_name":"Cornelia Caragea","orcid":"https://orcid.org/0000-0002-5664-2163"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cornelia Caragea","raw_affiliation_strings":["Iowa State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050678997","display_name":"Adrian Silvescu","orcid":null},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adrian Silvescu","raw_affiliation_strings":["Iowa State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067341711","display_name":"Doina Caragea","orcid":"https://orcid.org/0000-0002-6440-0914"},"institutions":[{"id":"https://openalex.org/I189590672","display_name":"Kansas State University","ror":"https://ror.org/05p1j8758","country_code":"US","type":"education","lineage":["https://openalex.org/I189590672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Doina Caragea","raw_affiliation_strings":["Kansas State University","Kansas state university"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kansas State University","institution_ids":["https://openalex.org/I189590672"]},{"raw_affiliation_string":"Kansas state university","institution_ids":["https://openalex.org/I189590672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004737962","display_name":"Vasant Honavar","orcid":"https://orcid.org/0000-0001-5399-3489"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vasant Honavar","raw_affiliation_strings":["Iowa State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University","institution_ids":["https://openalex.org/I173911158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10697282,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"257","last_page":"264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9976999759674072,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9976999759674072,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9965000152587891,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9952999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7505245208740234},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.7101355791091919},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6900463104248047},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.6768216490745544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6524553298950195},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5909456610679626},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.5857617259025574},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5080273151397705},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4939270615577698},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4379943907260895},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4307175874710083},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.42541757225990295},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.12876799702644348},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11335396766662598},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09127146005630493}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505245208740234},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.7101355791091919},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6900463104248047},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.6768216490745544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6524553298950195},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5909456610679626},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.5857617259025574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5080273151397705},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4939270615577698},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4379943907260895},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4307175874710083},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.42541757225990295},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.12876799702644348},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11335396766662598},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09127146005630493},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1854776.1854813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1854776.1854813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.185.2534","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.185.2534","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.iastate.edu/%7Ehonavar/Papers/acmbcb10.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7726334816","display_name":null,"funder_award_id":"IIS0711356","funder_id":"https://openalex.org/F4320337389","funder_display_name":"Division of Information and Intelligent Systems"}],"funders":[{"id":"https://openalex.org/F4320337389","display_name":"Division of Information and Intelligent Systems","ror":"https://ror.org/053a2cp42"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W146900863","https://openalex.org/W1498183065","https://openalex.org/W1990334093","https://openalex.org/W1991315287","https://openalex.org/W1994920552","https://openalex.org/W2002806284","https://openalex.org/W2009570821","https://openalex.org/W2026480276","https://openalex.org/W2048679005","https://openalex.org/W2049633694","https://openalex.org/W2053943711","https://openalex.org/W2058839892","https://openalex.org/W2071085454","https://openalex.org/W2097089247","https://openalex.org/W2104156750","https://openalex.org/W2104290444","https://openalex.org/W2107008379","https://openalex.org/W2112995888","https://openalex.org/W2115013148","https://openalex.org/W2126727667","https://openalex.org/W2127358574","https://openalex.org/W2136490963","https://openalex.org/W2141923507","https://openalex.org/W2145494108","https://openalex.org/W2146950091","https://openalex.org/W2147206923","https://openalex.org/W2149950545","https://openalex.org/W2152458080","https://openalex.org/W2153409933","https://openalex.org/W2160072419","https://openalex.org/W2161813919","https://openalex.org/W2167277498","https://openalex.org/W2595697910","https://openalex.org/W2799002609","https://openalex.org/W2997701990","https://openalex.org/W2999905431","https://openalex.org/W6629670641","https://openalex.org/W6751060025","https://openalex.org/W7039386316"],"related_works":["https://openalex.org/W4312414840","https://openalex.org/W2794908468","https://openalex.org/W4206276646","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W3004135598","https://openalex.org/W2952937263","https://openalex.org/W2131153761"],"abstract_inverted_index":{"Supervised":[0],"methods":[1,55],"for":[2],"learning":[3],"sequence":[4],"classifiers":[5],"rely":[6],"on":[7,140],"the":[8,22,30,32,42,90,104,164,167],"availability":[9],"of":[10,13,21,34,44,61,68,92,113,127,132,137],"large":[11,59],"amounts":[12,60,67],"labeled":[14,35,69],"data.":[15,46,70,97],"However,":[16],"in":[17,28,53,94],"many":[18],"applications":[19],"because":[20],"high":[23],"cost":[24],"and":[25,116,157,166],"effort":[26],"involved":[27],"labeling":[29],"data,":[31],"amount":[33,43],"data":[36,63],"is":[37,49],"quite":[38],"small":[39,66],"compared":[40],"to":[41,85,124],"unlabeled":[45,62,96,114,133,155],"Hence,":[47],"there":[48],"a":[50,76],"growing":[51],"interest":[52],"semi-supervised":[54,86,100,125,149,170],"that":[56,148],"can":[57,152],"exploit":[58,154],"together":[64],"with":[65],"In":[71],"this":[72],"paper,":[73],"we":[74],"introduce":[75],"novel":[77],"Abstraction":[78],"Augmented":[79],"Markov":[80,105],"Model":[81],"(AAMM)":[82],"based":[83,122,169],"approach":[84,123],"learning.":[87],"We":[88,98],"investigate":[89],"effectiveness":[91],"AAMMs":[93,101],"exploiting":[95],"compare":[99],"with:":[102],"(i)":[103,151],"models":[106],"(MMs)":[107],"(which":[108],"do":[109],"not":[110],"take":[111],"advantage":[112],"data);":[115],"(ii)":[117,158],"an":[118],"expectation":[119],"maximization":[120],"(EM)":[121],"training":[126],"MMs":[128,165],"(that":[129],"make":[130],"use":[131],"data).":[134],"The":[135],"results":[136],"our":[138],"experiments":[139],"three":[141],"protein":[142],"subcellular":[143],"localization":[144],"prediction":[145],"tasks":[146],"show":[147],"AAMMs:":[150],"effectively":[153],"data;":[156],"are":[159],"more":[160],"accurate":[161],"than":[162],"both":[163],"EM":[168],"MMs.":[171]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}