{"id":"https://openalex.org/W2101103777","doi":"https://doi.org/10.1109/cibcb.2007.4221262","title":"Extracting Efficient Fuzzy If-Then Rules from Mass Spectra of Blood Samples to Early Diagnosis of Ovarian Cancer","display_name":"Extracting Efficient Fuzzy If-Then Rules from Mass Spectra of Blood Samples to Early Diagnosis of Ovarian Cancer","publication_year":2007,"publication_date":"2007-04-01","ids":{"openalex":"https://openalex.org/W2101103777","doi":"https://doi.org/10.1109/cibcb.2007.4221262","mag":"2101103777"},"language":"en","primary_location":{"id":"doi:10.1109/cibcb.2007.4221262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cibcb.2007.4221262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE Symposium on Computational Intelligence and 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/A5021138197","display_name":"Amin Assareh","orcid":null},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"A. Assareh","raw_affiliation_strings":["Amirkabir University of Technology\uc2a0, Tehran, Iran","[Fac. of Biomed. Eng., AmirKabir Univ. of Technol., Tehran]"],"affiliations":[{"raw_affiliation_string":"Amirkabir University of Technology\uc2a0, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]},{"raw_affiliation_string":"[Fac. of Biomed. Eng., AmirKabir Univ. of Technol., Tehran]","institution_ids":["https://openalex.org/I158248296"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037720208","display_name":"Mohammad Hassan Moradi","orcid":"https://orcid.org/0000-0002-3386-4003"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"M.H. Moradi","raw_affiliation_strings":["Amirkabir University of Technology\uc2a0, Tehran, Iran","[Fac. of Biomed. Eng., AmirKabir Univ. of Technol., Tehran]"],"affiliations":[{"raw_affiliation_string":"Amirkabir University of Technology\uc2a0, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]},{"raw_affiliation_string":"[Fac. of Biomed. Eng., AmirKabir Univ. of Technol., Tehran]","institution_ids":["https://openalex.org/I158248296"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021138197"],"corresponding_institution_ids":["https://openalex.org/I158248296"],"apc_list":null,"apc_paid":null,"fwci":1.0239,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.74417253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10519","display_name":"Advanced Proteomics Techniques and Applications","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10519","display_name":"Advanced Proteomics Techniques and Applications","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9890999794006348,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9879000186920166,"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/computer-science","display_name":"Computer science","score":0.654426634311676},{"id":"https://openalex.org/keywords/biomarker","display_name":"Biomarker","score":0.606377363204956},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5938271284103394},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5482544898986816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5183764100074768},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5093863606452942},{"id":"https://openalex.org/keywords/biomarker-discovery","display_name":"Biomarker discovery","score":0.504198431968689},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5023343563079834},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4780728816986084},{"id":"https://openalex.org/keywords/mass-spectrum","display_name":"Mass spectrum","score":0.4661349654197693},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4485611915588379},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42488640546798706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37868571281433105},{"id":"https://openalex.org/keywords/mass-spectrometry","display_name":"Mass spectrometry","score":0.3438560366630554},{"id":"https://openalex.org/keywords/proteomics","display_name":"Proteomics","score":0.2658698558807373},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.10896921157836914},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09734788537025452},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.06834077835083008}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.654426634311676},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.606377363204956},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5938271284103394},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5482544898986816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5183764100074768},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5093863606452942},{"id":"https://openalex.org/C124535831","wikidata":"https://www.wikidata.org/wiki/Q4915074","display_name":"Biomarker discovery","level":4,"score":0.504198431968689},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5023343563079834},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4780728816986084},{"id":"https://openalex.org/C40325409","wikidata":"https://www.wikidata.org/wiki/Q2360668","display_name":"Mass spectrum","level":3,"score":0.4661349654197693},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4485611915588379},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42488640546798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37868571281433105},{"id":"https://openalex.org/C162356407","wikidata":"https://www.wikidata.org/wiki/Q180809","display_name":"Mass spectrometry","level":2,"score":0.3438560366630554},{"id":"https://openalex.org/C46111723","wikidata":"https://www.wikidata.org/wiki/Q471857","display_name":"Proteomics","level":3,"score":0.2658698558807373},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.10896921157836914},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09734788537025452},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06834077835083008},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cibcb.2007.4221262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cibcb.2007.4221262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1537494447","https://openalex.org/W1553483187","https://openalex.org/W2011716229","https://openalex.org/W2026301394","https://openalex.org/W2118645026","https://openalex.org/W2125730438","https://openalex.org/W2127199904","https://openalex.org/W2138870100","https://openalex.org/W2144639743","https://openalex.org/W2504016189"],"related_works":["https://openalex.org/W2345184372","https://openalex.org/W2592385986","https://openalex.org/W2515280043","https://openalex.org/W3185455591","https://openalex.org/W2022684485","https://openalex.org/W2510416153","https://openalex.org/W3197541072","https://openalex.org/W2699360047","https://openalex.org/W1987967032","https://openalex.org/W1578823017"],"abstract_inverted_index":{"Among":[0],"the":[1,19,40,58,64,100],"many":[2,41],"applications":[3],"of":[4,28,68,119],"mass":[5,12],"spectrometry,":[6],"biomarker":[7,55],"pattern":[8,70],"discovery":[9],"from":[10],"protein":[11],"spectra":[13],"has":[14],"aroused":[15],"huge":[16],"interest":[17],"in":[18,103],"recent":[20],"years.":[21],"While":[22],"research":[23],"efforts":[24],"have":[25,35],"raised":[26],"hopes":[27],"early":[29],"and":[30],"less":[31],"invasive":[32],"diagnosis,":[33],"they":[34],"also":[36],"brought":[37],"to":[38,43,74],"light":[39],"issues":[42],"be":[44],"tackled":[45],"before":[46],"mass-spectra-based":[47],"proteomic":[48],"patterns":[49],"become":[50],"routine":[51],"clinical":[52],"tools.":[53],"Undoubtedly,":[54],"selection":[56,85],"among":[57],"high":[59],"dimensional":[60],"input":[61],"data":[62,90],"is":[63],"most":[65],"critical":[66],"part":[67],"each":[69],"recognition":[71],"algorithm":[72],"applied":[73],"this":[75,78],"area.":[76],"In":[77],"paper":[79],"we":[80,111],"pursued":[81],"a":[82,113,117],"new":[83],"feature":[84],"strategy":[86],"that":[87],"explores":[88],"all":[89],"points":[91],"as":[92],"initial":[93],"features":[94,102],"rather":[95],"than":[96],"just":[97],"peaks.":[98],"Using":[99],"derived":[101],"conjunction":[104],"with":[105],"only":[106],"two":[107],"intuitive":[108],"fuzzy":[109],"rules,":[110],"achieved":[112],"considerable":[114],"accuracy":[115],"over":[116],"couple":[118],"well-known":[120],"ovarian":[121],"cancer":[122],"datasets":[123]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}