{"id":"https://openalex.org/W4385063428","doi":"https://doi.org/10.1145/3589437.3589439","title":"Prediction of Protein Interactions Based on Cnn-Lstm","display_name":"Prediction of Protein Interactions Based on Cnn-Lstm","publication_year":2022,"publication_date":"2022-12-26","ids":{"openalex":"https://openalex.org/W4385063428","doi":"https://doi.org/10.1145/3589437.3589439"},"language":"en","primary_location":{"id":"doi:10.1145/3589437.3589439","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3589437.3589439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Computational Biology and Bioinformatics","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/A5089401782","display_name":"Jihong Wang","orcid":"https://orcid.org/0000-0002-9652-0072"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jihong Wang","raw_affiliation_strings":["Guangdong University of Eduaction, China"],"raw_orcid":"https://orcid.org/0000-0002-9652-0072","affiliations":[{"raw_affiliation_string":"Guangdong University of Eduaction, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069640764","display_name":"Xiaodan Wang","orcid":"https://orcid.org/0000-0001-8553-309X"},"institutions":[{"id":"https://openalex.org/I190242452","display_name":"Guangdong Pharmaceutical University","ror":"https://ror.org/02vg7mz57","country_code":"CN","type":"education","lineage":["https://openalex.org/I190242452"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodan Wang,","raw_affiliation_strings":["Guangdong Pharmaceutical University, China"],"raw_orcid":"https://orcid.org/0000-0001-8553-309X","affiliations":[{"raw_affiliation_string":"Guangdong Pharmaceutical University, China","institution_ids":["https://openalex.org/I190242452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101431146","display_name":"Junwei Wu","orcid":"https://orcid.org/0009-0000-2297-3864"},"institutions":[{"id":"https://openalex.org/I190242452","display_name":"Guangdong Pharmaceutical University","ror":"https://ror.org/02vg7mz57","country_code":"CN","type":"education","lineage":["https://openalex.org/I190242452"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junwei Wu","raw_affiliation_strings":["Guangdong Pharmaceutical University, China"],"raw_orcid":"https://orcid.org/0009-0000-2297-3864","affiliations":[{"raw_affiliation_string":"Guangdong Pharmaceutical University, China","institution_ids":["https://openalex.org/I190242452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":null,"issue":null,"first_page":"7","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.993399977684021,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9921000003814697,"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":0.7587317228317261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6999408602714539},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6722573041915894},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6513089537620544},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6093314290046692},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5928192138671875},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.488974928855896},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4844343960285187},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47153669595718384},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4520622491836548},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4492744207382202},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.4414728879928589},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4273822605609894},{"id":"https://openalex.org/keywords/protein-sequencing","display_name":"Protein sequencing","score":0.4103076457977295},{"id":"https://openalex.org/keywords/peptide-sequence","display_name":"Peptide sequence","score":0.20513862371444702},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.1872004270553589},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.1043836772441864},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08996018767356873}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7587317228317261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6999408602714539},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6722573041915894},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6513089537620544},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6093314290046692},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5928192138671875},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.488974928855896},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4844343960285187},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47153669595718384},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4520622491836548},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4492744207382202},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.4414728879928589},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4273822605609894},{"id":"https://openalex.org/C10010492","wikidata":"https://www.wikidata.org/wiki/Q3142557","display_name":"Protein sequencing","level":4,"score":0.4103076457977295},{"id":"https://openalex.org/C167625842","wikidata":"https://www.wikidata.org/wiki/Q899763","display_name":"Peptide sequence","level":3,"score":0.20513862371444702},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.1872004270553589},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.1043836772441864},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08996018767356873},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589437.3589439","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3589437.3589439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Computational Biology and Bioinformatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2055027431","https://openalex.org/W2592340788","https://openalex.org/W2885583144","https://openalex.org/W2890911678","https://openalex.org/W2914020633","https://openalex.org/W2944679105","https://openalex.org/W2952253955","https://openalex.org/W2957436444","https://openalex.org/W2960293113","https://openalex.org/W3045623890","https://openalex.org/W4220933909"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3048601286","https://openalex.org/W2965925734"],"abstract_inverted_index":{"Protein":[0],"is":[1,15,170,180,199,217,235,239],"the":[2,6,17,36,44,76,105,132,144,160,164,167,175,183,187,208,215,231],"material":[3,18],"basis":[4,19],"and":[5,13,24,46,57,74,93,114,120,126,136,174,182,186,204,227,237,241,255,258],"only":[7,33],"form":[8],"of":[9,29,38,48,80,107,134,146,214],"all":[10],"life":[11],"activities,":[12],"it":[14,248],"also":[16,41],"or":[20],"drug":[21,90],"for":[22,192,201,263],"diagnosing":[23],"treating":[25],"diseases.":[26],"The":[27,211],"number":[28,37],"human":[30],"proteins":[31],"not":[32],"far":[34,54],"exceeds":[35],"genes,":[39],"but":[40],"due":[42],"to":[43,87,156,207],"variability":[45],"diversity":[47],"proteins,":[49],"protein":[50,161,168,176],"research":[51,102,139,165],"techniques":[52],"are":[53,190],"more":[55],"complex":[56],"difficult":[58],"than":[59],"nucleic":[60],"acid":[61],"techniques.":[62],"Protein-protein":[63],"interactions":[64],"(PPIs)":[65],"play":[66],"key":[67],"roles":[68],"in":[69,89,104,131,143,172],"many":[70,101],"cellular":[71],"biological":[72],"processes":[73],"underlie":[75],"entire":[77],"molecular":[78],"machinery":[79],"living":[81],"cells,":[82],"which":[83],"can":[84,140,259],"be":[85,141,260],"used":[86,200,246,262],"aid":[88],"target":[91],"detection":[92],"therapeutic":[94],"design.":[95],"Deep":[96],"learning":[97,124,225,254],"methods":[98,113,116],"have":[99,117,127],"produced":[100],"results":[103,130],"field":[106,145],"bioinformatics.":[108],"Convolutional":[109],"neural":[110],"network":[111,198],"(CNN)":[112],"LSTM":[115,188,209],"strong":[118],"spatial":[119],"sequence":[121,162,169],"feature":[122],"representation":[123,202],"capabilities,":[125,257],"achieved":[128],"outstanding":[129],"fields":[133],"images":[135],"text.":[137],"In-depth":[138],"done":[142],"PPI.":[147,158],"In":[148],"this":[149],"paper,":[150],"we":[151],"propose":[152],"a":[153],"CNN-LSTM":[154,251],"method":[155,185,189],"predict":[157],"Taking":[159],"as":[163,224],"basis,":[166],"encoded":[171],"hexadecimal,":[173],"interaction":[177],"relationship":[178],"pair":[179],"constructed,":[181],"CNN":[184],"introduced":[191],"fusion":[193],"learning.":[194],"A":[195],"3-layer":[196],"convolutional":[197],"learning,":[203],"then":[205],"connected":[206],"layer.":[210],"prediction":[212],"performance":[213],"model":[216],"improved":[218],"by":[219],"adjusting":[220],"different":[221],"parameters":[222],"such":[223],"rate":[226],"activation":[228],"function.":[229],"On":[230],"test":[232],"set,":[233],"Auc":[234],"0.9212":[236],"F1":[238],"0.9206,":[240],"compared":[242],"with":[243],"other":[244],"commonly":[245],"models,":[247],"proves":[249],"that":[250],"has":[252],"good":[253],"generalization":[256],"effectively":[261],"PPI":[264],"prediction.":[265]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}