{"id":"https://openalex.org/W3044620404","doi":"https://doi.org/10.1109/access.2020.3010944","title":"A Pearson Based Feature Compressing Model for SNARE Protein Classification","display_name":"A Pearson Based Feature Compressing Model for SNARE Protein Classification","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3044620404","doi":"https://doi.org/10.1109/access.2020.3010944","mag":"3044620404"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3010944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3010944","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2020.3010944","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100601430","display_name":"Guilin Li","orcid":"https://orcid.org/0000-0001-7301-5391"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guilin Li","raw_affiliation_strings":["Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100601430"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0829,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.43939935,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"136560","last_page":"136569"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9998000264167786,"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":0.9998000264167786,"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/T10617","display_name":"Cellular transport and secretion","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell 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/T13326","display_name":"Biochemical and Structural Characterization","score":0.988099992275238,"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/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.8046540021896362},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7438796162605286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6852363348007202},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.680380642414093},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5925717353820801},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5791596174240112},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5723530650138855},{"id":"https://openalex.org/keywords/pearsons-chi-squared-test","display_name":"Pearson's chi-squared test","score":0.5008978843688965},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4990408420562744},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4334254264831543},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4062156677246094},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16728931665420532},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1292157769203186},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.12512800097465515}],"concepts":[{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.8046540021896362},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7438796162605286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6852363348007202},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.680380642414093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5925717353820801},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5791596174240112},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5723530650138855},{"id":"https://openalex.org/C66924754","wikidata":"https://www.wikidata.org/wiki/Q2336256","display_name":"Pearson's chi-squared test","level":4,"score":0.5008978843688965},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4990408420562744},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4334254264831543},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4062156677246094},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16728931665420532},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1292157769203186},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.12512800097465515},{"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/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3010944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3010944","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:99df8c416920466c9e8b3268ddee485f","is_oa":true,"landing_page_url":"https://doaj.org/article/99df8c416920466c9e8b3268ddee485f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 136560-136569 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3010944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3010944","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W98466101","https://openalex.org/W1516738772","https://openalex.org/W1525145973","https://openalex.org/W1951204040","https://openalex.org/W1982267716","https://openalex.org/W1984794455","https://openalex.org/W1996768014","https://openalex.org/W2000302106","https://openalex.org/W2005786380","https://openalex.org/W2010688739","https://openalex.org/W2027514285","https://openalex.org/W2037007905","https://openalex.org/W2042084565","https://openalex.org/W2047672715","https://openalex.org/W2059926522","https://openalex.org/W2100453242","https://openalex.org/W2132581719","https://openalex.org/W2132797856","https://openalex.org/W2133462743","https://openalex.org/W2133990480","https://openalex.org/W2135628402","https://openalex.org/W2154139219","https://openalex.org/W2161062388","https://openalex.org/W2161732286","https://openalex.org/W2165733650","https://openalex.org/W2488854198","https://openalex.org/W2503226155","https://openalex.org/W2554725900","https://openalex.org/W2575228167","https://openalex.org/W2575552627","https://openalex.org/W2588614855","https://openalex.org/W2592644437","https://openalex.org/W2593645525","https://openalex.org/W2599052511","https://openalex.org/W2608969085","https://openalex.org/W2622004629","https://openalex.org/W2757915849","https://openalex.org/W2769032875","https://openalex.org/W2774506992","https://openalex.org/W2793168264","https://openalex.org/W2793278326","https://openalex.org/W2807018623","https://openalex.org/W2808487499","https://openalex.org/W2881233020","https://openalex.org/W2888906255","https://openalex.org/W2899075300","https://openalex.org/W2900134604","https://openalex.org/W2903705089","https://openalex.org/W2910596642","https://openalex.org/W2922312159","https://openalex.org/W2946099214","https://openalex.org/W2954364924","https://openalex.org/W2972223935","https://openalex.org/W2991266812","https://openalex.org/W2994989159"],"related_works":["https://openalex.org/W3182009020","https://openalex.org/W2811390910","https://openalex.org/W2146076056","https://openalex.org/W2144059113","https://openalex.org/W2563096758","https://openalex.org/W2964383635","https://openalex.org/W3201385912","https://openalex.org/W3003836766","https://openalex.org/W3010923102","https://openalex.org/W2546942002"],"abstract_inverted_index":{"SNARE":[0,26,49,70],"proteins":[1,6,27,50],"are":[2,63,92,114,130],"a":[3,31,38,134],"group":[4],"of":[5,12,24,33,77,120,141,163,180],"that":[7,154],"drive":[8],"the":[9,25,48,69,75,81,99,118,124,139,142,155,160,167,174],"biological":[10],"fusion":[11],"two":[13],"membranes.":[14],"It":[15,94],"is":[16,44,84,95,145],"important":[17],"to":[18,30,46,65,97,116,132],"identify":[19,47],"them":[20],"accurately,":[21],"because":[22],"malfunction":[23],"can":[28,172],"lead":[29],"lot":[32],"diseases.":[34],"In":[35],"this":[36],"paper,":[37],"Pearson":[39,108,168],"based":[40,157],"feature":[41,60,101,111,122,125,169],"compressing":[42],"model":[43,158],"proposed":[45],"accurately":[51],"and":[52,58,71,107,138],"efficiently.":[53],"First,":[54],"188D,":[55],"CKSAAP,":[56],"CTDD":[57,156],"CTRIAD":[59],"extraction":[61],"methods":[62,83,113],"used":[64,115,131],"extract":[66],"features":[67,78,91,129,144,164],"from":[68],"non-SNARE":[72],"proteins.":[73],"As":[74],"number":[76],"extracted":[79],"by":[80,147,166],"four":[82],"very":[85],"large,":[86],"which":[87],"means":[88],"many":[89],"redundant":[90],"included.":[93],"necessary":[96],"filter":[98],"original":[100],"set.":[102,126],"The":[103,127,150],"Chi-Square,":[104],"Information":[105],"Gain":[106],"Correlation":[109],"Coefficient":[110],"selection":[112,170],"evaluate":[117],"value":[119],"each":[121],"in":[123],"selected":[128,143,165],"train":[133],"random":[135],"forest":[136],"classifier":[137],"performance":[140,176],"evaluated":[146],"cross":[148],"validation.":[149],"experimental":[151],"results":[152],"showed":[153],"with":[159],"first":[161],"70%":[162],"method":[171],"achieve":[173],"best":[175],"among":[177],"all":[178],"kinds":[179],"models.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
