{"id":"https://openalex.org/W4313413436","doi":"https://doi.org/10.1109/bibm55620.2022.9995071","title":"EMDS: predicting essential miRNAs based on deep learning and sequences","display_name":"EMDS: predicting essential miRNAs based on deep learning and sequences","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313413436","doi":"https://doi.org/10.1109/bibm55620.2022.9995071"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9995071","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995071","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100613014","display_name":"Cheng Yan","orcid":"https://orcid.org/0000-0002-5021-6841"},"institutions":[{"id":"https://openalex.org/I14992734","display_name":"Hunan University of Traditional Chinese Medicine","ror":"https://ror.org/05qfq0x09","country_code":"CN","type":"education","lineage":["https://openalex.org/I14992734"]},{"id":"https://openalex.org/I4210167317","display_name":"Qiannan Normal College For Nationalities","ror":"https://ror.org/05szpc322","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210167317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Yan","raw_affiliation_strings":["Hunan University of Chinese Medicine,School of Computer Science and Engineering,Changsha,China","School of Computer Science and Engineering, Hunan University of Chinese Medicine, Changsha, China","School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University of Chinese Medicine,School of Computer Science and Engineering,Changsha,China","institution_ids":["https://openalex.org/I14992734"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Hunan University of Chinese Medicine, Changsha, China","institution_ids":["https://openalex.org/I14992734"]},{"raw_affiliation_string":"School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, China","institution_ids":["https://openalex.org/I4210167317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026877800","display_name":"Guihua Duan","orcid":"https://orcid.org/0000-0002-9406-6443"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guihua Duan","raw_affiliation_strings":["Central South University,School of Computer Science and Engineering,Changsha,China","School of Computer Science and Engineering, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central South University,School of Computer Science and Engineering,Changsha,China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091157395","display_name":"Fang\u2010Xiang Wu","orcid":"https://orcid.org/0000-0002-4593-9332"},"institutions":[{"id":"https://openalex.org/I32625721","display_name":"University of Saskatchewan","ror":"https://ror.org/010x8gc63","country_code":"CA","type":"education","lineage":["https://openalex.org/I32625721"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fang-Xiang Wu","raw_affiliation_strings":["University of Saskatchewan,Division of Biomedical Engineering and Department of Mechanical Engineering,Saskatoon,Canada","Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Saskatchewan,Division of Biomedical Engineering and Department of Mechanical Engineering,Saskatoon,Canada","institution_ids":["https://openalex.org/I32625721"]},{"raw_affiliation_string":"Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada","institution_ids":["https://openalex.org/I32625721"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"107","issue":null,"first_page":"613","last_page":"618"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10062","display_name":"MicroRNA in disease regulation","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"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/T10062","display_name":"MicroRNA in disease regulation","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"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/T10515","display_name":"Cancer-related molecular mechanisms research","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"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/T11482","display_name":"RNA modifications and cancer","score":0.9962000250816345,"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/subsequence","display_name":"Subsequence","score":0.7291106581687927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7072424292564392},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.683925211429596},{"id":"https://openalex.org/keywords/longest-common-subsequence-problem","display_name":"Longest common subsequence problem","score":0.5991388559341431},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5832974910736084},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5279892086982727},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5122682452201843},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4571409821510315},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41639450192451477},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3626753091812134},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14852789044380188},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10133591294288635}],"concepts":[{"id":"https://openalex.org/C137877099","wikidata":"https://www.wikidata.org/wiki/Q1332977","display_name":"Subsequence","level":3,"score":0.7291106581687927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7072424292564392},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.683925211429596},{"id":"https://openalex.org/C120098539","wikidata":"https://www.wikidata.org/wiki/Q141001","display_name":"Longest common subsequence problem","level":2,"score":0.5991388559341431},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5832974910736084},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5279892086982727},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5122682452201843},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4571409821510315},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41639450192451477},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3626753091812134},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14852789044380188},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10133591294288635},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9995071","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995071","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.6600000262260437}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W14098964","https://openalex.org/W1678356000","https://openalex.org/W1980269624","https://openalex.org/W2022646895","https://openalex.org/W2046240326","https://openalex.org/W2046710525","https://openalex.org/W2077327925","https://openalex.org/W2101234009","https://openalex.org/W2103151642","https://openalex.org/W2103250001","https://openalex.org/W2122915854","https://openalex.org/W2128728535","https://openalex.org/W2128768066","https://openalex.org/W2133564696","https://openalex.org/W2142437744","https://openalex.org/W2148279834","https://openalex.org/W2153635508","https://openalex.org/W2155669068","https://openalex.org/W2160467221","https://openalex.org/W2168750261","https://openalex.org/W2170722275","https://openalex.org/W2171296186","https://openalex.org/W2418459390","https://openalex.org/W2768053821","https://openalex.org/W2768348081","https://openalex.org/W2770062471","https://openalex.org/W2771019114","https://openalex.org/W2786672974","https://openalex.org/W2889313194","https://openalex.org/W2964446853","https://openalex.org/W3012154856","https://openalex.org/W3093996932","https://openalex.org/W3146565636","https://openalex.org/W3159261411","https://openalex.org/W4210607727","https://openalex.org/W4211064852","https://openalex.org/W4221083137","https://openalex.org/W4246271082","https://openalex.org/W6675354045","https://openalex.org/W6679434410","https://openalex.org/W6745609711","https://openalex.org/W6748816842"],"related_works":["https://openalex.org/W25732909","https://openalex.org/W4323338832","https://openalex.org/W4289596129","https://openalex.org/W3083981518","https://openalex.org/W170643605","https://openalex.org/W4225868058","https://openalex.org/W1999879627","https://openalex.org/W2088055539","https://openalex.org/W1574834681","https://openalex.org/W2389167168"],"abstract_inverted_index":{"MicroRNAs":[0],"(miRNAs)":[1],"as":[2,180],"small":[3],"19-":[4],"to":[5,29,45,82,107,163,193],"24-nucleotide":[6],"noncoding":[7],"RNAs":[8],"play":[9],"crucial":[10],"roles":[11],"in":[12,97,151,247],"some":[13,39],"key":[14],"biological":[15,51],"progress":[16],"associated":[17],"with":[18,214],"human":[19],"diseases.":[20,36],"Therefore,":[21,96],"identifying":[22],"the":[23,32,64,84,89,115,124,129,142,149,172,199,206,265],"essentiality":[24],"of":[25,35,70,76,120,132,145,167,202,249],"miRNAs":[26,48],"is":[27,94,185],"important":[28],"systematically":[30],"understand":[31],"pathogenic":[33],"mechanism":[34,147,162],"There":[37],"are":[38,53,155],"computational":[40,60,103],"methods":[41,61,218],"have":[42],"been":[43],"developed":[44],"predict":[46,108,194,264],"essential":[47,109,195,266],"because":[49],"traditional":[50],"experiments":[52],"both":[54],"time-":[55],"and":[56,67,117,134,148,175,227],"labor-consuming.":[57],"However,":[58],"these":[59],"only":[62,114],"used":[63],"statistical":[65,116,173],"feature":[66,69],"structural":[68,118,176],"miRNA":[71,153,182],"sequences.":[72],"The":[73,238],"timing":[74],"characteristics":[75,131],"sequences":[77,121,133],"also":[78,123,211,257],"should":[79],"be":[80],"considered":[81],"improve":[83],"prediction":[85,200,245],"performance.":[86],"In":[87],"addition,":[88],"capability":[90],"deep":[91],"learning":[92],"model":[93],"well-known.":[95],"this":[98],"study,":[99],"we":[100,157,170],"present":[101],"a":[102,152,159],"method":[104,204,261],"(called":[105],"EMDS)":[106],"miRNAs.":[110,168,196,267],"EMDS":[111,213,242],"takes":[112],"not":[113],"features":[119,126,166,174,179,183],"but":[122],"subsequence":[125,150,165,178],"based":[127],"on":[128],"time":[130],"Convolutional":[135],"Neural":[136],"Networks":[137],"(CNN).":[138],"Furthermore,":[139],"considering":[140],"that":[141,241,259],"successful":[143],"applications":[144],"attention":[146,161],"sequence":[154],"important,":[156],"use":[158],"neural":[160],"obtain":[164],"Finally,":[169],"integrate":[171],"features,":[177],"final":[181],"which":[184,219],"inputted":[186],"into":[187],"Light":[188],"Gradient":[189],"Boosting":[190],"Machine":[191,230],"(LGBM)":[192],"We":[197,210],"evaluate":[198],"performance":[201,246],"our":[203,260],"by":[205,232],"5-fold":[207],"cross":[208,235],"validation.":[209],"compare":[212],"other":[215],"four":[216],"competing":[217],"include":[220],"PESM,":[221],"miES,":[222],"Gaussian":[223],"Naive":[224],"Bayes":[225],"(Gaus_NB)":[226],"Support":[228],"Vector":[229],"(SVM)":[231],"performing":[233],"same":[234],"validation":[236],"experiments.":[237],"results":[239],"show":[240],"achieves":[243],"better":[244],"terms":[248],"AUC":[250],"(EMDS:0.9335,":[251],"PESM:0.9117,":[252],"miES:0.8837,":[253],"Gaus_NB:0.8720,":[254],"SVM:0.8571).":[255],"It":[256],"illustrates":[258],"can":[262],"effectively":[263]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}