{"id":"https://openalex.org/W1984818259","doi":"https://doi.org/10.1109/cibcb.2012.6217230","title":"Blind identification of post-translational modifications via dynamic time warping model","display_name":"Blind identification of post-translational modifications via dynamic time warping model","publication_year":2012,"publication_date":"2012-05-01","ids":{"openalex":"https://openalex.org/W1984818259","doi":"https://doi.org/10.1109/cibcb.2012.6217230","mag":"1984818259"},"language":"en","primary_location":{"id":"doi:10.1109/cibcb.2012.6217230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cibcb.2012.6217230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","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/A5100423968","display_name":"Hui Li","orcid":"https://orcid.org/0000-0003-2600-4838"},"institutions":[{"id":"https://openalex.org/I137853757","display_name":"Howard University","ror":"https://ror.org/05gt1vc06","country_code":"US","type":"education","lineage":["https://openalex.org/I137853757"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hui Li","raw_affiliation_strings":["Department of Systems and Computer Science, Howard University College of Medicine, Washington DC, DC, USA","Department of Systems and Computer Science, Howard University, Washington DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Systems and Computer Science, Howard University College of Medicine, Washington DC, DC, USA","institution_ids":["https://openalex.org/I137853757"]},{"raw_affiliation_string":"Department of Systems and Computer Science, Howard University, Washington DC, USA","institution_ids":["https://openalex.org/I137853757"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115590267","display_name":"Chunmei Liu","orcid":"https://orcid.org/0000-0001-9560-3709"},"institutions":[{"id":"https://openalex.org/I137853757","display_name":"Howard University","ror":"https://ror.org/05gt1vc06","country_code":"US","type":"education","lineage":["https://openalex.org/I137853757"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chunmei Liu","raw_affiliation_strings":["Department of Systems and Computer Science, Howard University College of Medicine, Washington DC, DC, USA","Department of Systems and Computer Science, Howard University, Washington DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Systems and Computer Science, Howard University College of Medicine, Washington DC, DC, USA","institution_ids":["https://openalex.org/I137853757"]},{"raw_affiliation_string":"Department of Systems and Computer Science, Howard University, Washington DC, USA","institution_ids":["https://openalex.org/I137853757"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042203775","display_name":"William M. Southerland","orcid":null},"institutions":[{"id":"https://openalex.org/I137853757","display_name":"Howard University","ror":"https://ror.org/05gt1vc06","country_code":"US","type":"education","lineage":["https://openalex.org/I137853757"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William Southerland","raw_affiliation_strings":["Department of Biochemistry, Howard University College of Medicine, Washington DC, DC, USA","Department of Biochemistry, Howard University, Washington DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biochemistry, Howard University College of Medicine, Washington DC, DC, USA","institution_ids":["https://openalex.org/I137853757"]},{"raw_affiliation_string":"Department of Biochemistry, Howard University, Washington DC, USA","institution_ids":["https://openalex.org/I137853757"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100423968"],"corresponding_institution_ids":["https://openalex.org/I137853757"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05745052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"192","last_page":"197"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9984999895095825,"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/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9984999895095825,"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":0.9976000189781189,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.7387528419494629},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6810186505317688},{"id":"https://openalex.org/keywords/posttranslational-modification","display_name":"Posttranslational modification","score":0.5808793902397156},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.574289858341217},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.5733082294464111},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.5075722932815552},{"id":"https://openalex.org/keywords/proteomics","display_name":"Proteomics","score":0.45732346177101135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41935232281684875},{"id":"https://openalex.org/keywords/tandem-mass-spectrometry","display_name":"Tandem mass spectrometry","score":0.4145946800708771},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.37193137407302856},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3616172671318054},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3553142845630646},{"id":"https://openalex.org/keywords/mass-spectrometry","display_name":"Mass spectrometry","score":0.16609665751457214},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.13952305912971497},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.1387382447719574},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.08391228318214417}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.7387528419494629},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6810186505317688},{"id":"https://openalex.org/C100631289","wikidata":"https://www.wikidata.org/wiki/Q898362","display_name":"Posttranslational modification","level":3,"score":0.5808793902397156},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.574289858341217},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.5733082294464111},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.5075722932815552},{"id":"https://openalex.org/C46111723","wikidata":"https://www.wikidata.org/wiki/Q471857","display_name":"Proteomics","level":3,"score":0.45732346177101135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41935232281684875},{"id":"https://openalex.org/C31827203","wikidata":"https://www.wikidata.org/wiki/Q874296","display_name":"Tandem mass spectrometry","level":3,"score":0.4145946800708771},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.37193137407302856},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3616172671318054},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3553142845630646},{"id":"https://openalex.org/C162356407","wikidata":"https://www.wikidata.org/wiki/Q180809","display_name":"Mass spectrometry","level":2,"score":0.16609665751457214},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.13952305912971497},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.1387382447719574},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.08391228318214417},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C181199279","wikidata":"https://www.wikidata.org/wiki/Q8047","display_name":"Enzyme","level":2,"score":0.0},{"id":"https://openalex.org/C105580179","wikidata":"https://www.wikidata.org/wiki/Q188928","display_name":"Messenger RNA","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cibcb.2012.6217230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cibcb.2012.6217230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1564058497","https://openalex.org/W1971887998","https://openalex.org/W1981593008","https://openalex.org/W1982537978","https://openalex.org/W1982968560","https://openalex.org/W1986092967","https://openalex.org/W1995229312","https://openalex.org/W2001848433","https://openalex.org/W2008317045","https://openalex.org/W2023096047","https://openalex.org/W2034645824","https://openalex.org/W2035283832","https://openalex.org/W2038615001","https://openalex.org/W2059572899","https://openalex.org/W2070526549","https://openalex.org/W2082597019","https://openalex.org/W2102412629","https://openalex.org/W2147788028"],"related_works":["https://openalex.org/W1670332068","https://openalex.org/W2095618524","https://openalex.org/W2347413598","https://openalex.org/W2330863229","https://openalex.org/W71572444","https://openalex.org/W1997383766","https://openalex.org/W2350336482","https://openalex.org/W2036868240","https://openalex.org/W2078166165","https://openalex.org/W1830980872"],"abstract_inverted_index":{"Post-translation":[0],"modification":[1],"(PTM)":[2],"plays":[3],"a":[4,17,28,49,54,58,70],"significant":[5],"role":[6],"in":[7,21,57,69],"the":[8,38,102],"understanding":[9],"of":[10,67],"protein":[11],"functions.":[12],"However,":[13],"PTM":[14,84],"identification":[15],"is":[16,34,108],"very":[18],"challenging":[19],"problem":[20],"proteomics":[22],"research":[23],"field.":[24],"In":[25],"this":[26],"paper,":[27],"dynamic":[29],"time":[30],"warping":[31],"(DTW)":[32],"model":[33],"introduced":[35],"to":[36],"find":[37],"optimal":[39],"match":[40],"between":[41],"an":[42],"experimental":[43,77],"tandem":[44],"mass":[45],"spectrum":[46,51],"(MS/MS)":[47],"and":[48,85,96],"theoretical":[50],"generated":[52],"from":[53],"peptide":[55,59,99],"sequence":[56],"database.":[60],"Our":[61],"approach":[62,91],"can":[63,92],"identify":[64,93],"all":[65],"types":[66],"PTMs":[68,87],"blind":[71,113],"mode.":[72],"The":[73],"test":[74],"results":[75],"on":[76],"spectra":[78],"that":[79,89],"contain":[80],"no":[81],"PTM,":[82],"one":[83],"two":[86],"show":[88],"our":[90],"95%,":[94],"93%,":[95],"34%":[97],"correct":[98],"sequences":[100],"as":[101],"top":[103],"1":[104],"candidates,":[105],"respectively,":[106],"which":[107],"much":[109],"better":[110],"than":[111],"other":[112],"search":[114],"methods.":[115]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}