{"id":"https://openalex.org/W4390971121","doi":"https://doi.org/10.1109/bibm58861.2023.10385839","title":"TNFIPs-Net: A deep learning model based on multi-feature fusion for prediction of TNF-\u03b1 inducing epitopes","display_name":"TNFIPs-Net: A deep learning model based on multi-feature fusion for prediction of TNF-\u03b1 inducing epitopes","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4390971121","doi":"https://doi.org/10.1109/bibm58861.2023.10385839"},"language":"en","primary_location":{"id":"doi:10.1109/bibm58861.2023.10385839","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5100413412","display_name":"Shengli Zhang","orcid":"https://orcid.org/0000-0001-8786-0940"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shengli Zhang","raw_affiliation_strings":["Xidian University,School of Mathematics and Statistics,Xi’an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,School of Mathematics and Statistics,Xi’an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100297592","display_name":"Yujie Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujie Xu","raw_affiliation_strings":["Xidian University,School of Mathematics and Statistics,Xi’an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,School of Mathematics and Statistics,Xi’an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103323488","display_name":"Yuanyuan Jing","orcid":"https://orcid.org/0009-0006-0357-5274"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Jing","raw_affiliation_strings":["Xidian University,School of Mathematics and Statistics,Xi’an,China"],"affiliations":[{"raw_affiliation_string":"Xidian University,School of Mathematics and Statistics,Xi’an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008784102","display_name":"Yunyun Liang","orcid":"https://orcid.org/0000-0002-1749-564X"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunyun Liang","raw_affiliation_strings":["Xi’an Polytechnic University,School of Science,Xi’an,China"],"affiliations":[{"raw_affiliation_string":"Xi’an Polytechnic University,School of Science,Xi’an,China","institution_ids":["https://openalex.org/I27599042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100413412"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.3107,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65351593,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"978","last_page":"983"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.9998999834060669,"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/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.9998999834060669,"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":0.9987999796867371,"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/T10167","display_name":"Influenza Virus Research Studies","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/epitope","display_name":"Epitope","score":0.8163732290267944},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7025609016418457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5955640077590942},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5513435006141663},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5289074182510376},{"id":"https://openalex.org/keywords/tumor-necrosis-factor-alpha","display_name":"Tumor necrosis factor alpha","score":0.5212476849555969},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.42134395241737366},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36649274826049805},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33244627714157104},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.32970190048217773},{"id":"https://openalex.org/keywords/antigen","display_name":"Antigen","score":0.23751616477966309},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.15441808104515076},{"id":"https://openalex.org/keywords/immunology","display_name":"Immunology","score":0.10967510938644409}],"concepts":[{"id":"https://openalex.org/C195616568","wikidata":"https://www.wikidata.org/wiki/Q128711","display_name":"Epitope","level":3,"score":0.8163732290267944},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7025609016418457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5955640077590942},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5513435006141663},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5289074182510376},{"id":"https://openalex.org/C17991360","wikidata":"https://www.wikidata.org/wiki/Q21173843","display_name":"Tumor necrosis factor alpha","level":2,"score":0.5212476849555969},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.42134395241737366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36649274826049805},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33244627714157104},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.32970190048217773},{"id":"https://openalex.org/C147483822","wikidata":"https://www.wikidata.org/wiki/Q103537","display_name":"Antigen","level":2,"score":0.23751616477966309},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.15441808104515076},{"id":"https://openalex.org/C203014093","wikidata":"https://www.wikidata.org/wiki/Q101929","display_name":"Immunology","level":1,"score":0.10967510938644409},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm58861.2023.10385839","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/3","display_name":"Good health and well-being","score":0.8500000238418579}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1689711448","https://openalex.org/W2026535711","https://openalex.org/W2035104435","https://openalex.org/W2065511283","https://openalex.org/W2080221075","https://openalex.org/W2095013915","https://openalex.org/W2104804534","https://openalex.org/W2110485445","https://openalex.org/W2128728535","https://openalex.org/W2145957695","https://openalex.org/W2147800946","https://openalex.org/W2156275638","https://openalex.org/W2620675477","https://openalex.org/W2657631929","https://openalex.org/W2793168264","https://openalex.org/W2801121099","https://openalex.org/W2807818025","https://openalex.org/W2898389621","https://openalex.org/W2901314256","https://openalex.org/W2911789924","https://openalex.org/W2950389803","https://openalex.org/W2950635152","https://openalex.org/W2964874436","https://openalex.org/W2989917577","https://openalex.org/W3010877467","https://openalex.org/W3092555703","https://openalex.org/W3111318015","https://openalex.org/W3118324533","https://openalex.org/W3133056632","https://openalex.org/W3138964863","https://openalex.org/W3164453494","https://openalex.org/W3177828909","https://openalex.org/W4210810268","https://openalex.org/W4213149192","https://openalex.org/W4220662585","https://openalex.org/W4282926623","https://openalex.org/W4286560871","https://openalex.org/W4292438752","https://openalex.org/W4309409056","https://openalex.org/W4315621831","https://openalex.org/W4366490404","https://openalex.org/W4385245566","https://openalex.org/W6641231757","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W1979044599","https://openalex.org/W2021824670","https://openalex.org/W2009867644","https://openalex.org/W2363997565","https://openalex.org/W1981120125","https://openalex.org/W2905271011","https://openalex.org/W3164948662","https://openalex.org/W4289536128","https://openalex.org/W3153597579","https://openalex.org/W1872833176"],"abstract_inverted_index":{"Tumor":[0],"necrosis":[1,12],"factor":[2,13],"alpha":[3],"(TNF-\u03b1)":[4],"is":[5,26,156],"a":[6,17,59,102,120],"cytokine":[7],"belonging":[8],"to":[9,65,145,162,186],"the":[10,22,44,131,134,159,164,172,178,184,195],"tumor":[11],"family.":[14],"It":[15],"plays":[16],"crucial":[18],"regulatory":[19],"role":[20],"in":[21,28,48,158,207],"immune":[23,95],"system":[24],"and":[25,52,70,76,88,93,127,139,149,177,204],"involved":[27],"various":[29],"biological":[30],"processes.":[31],"TNF-\u03b1":[32,47,54,82,113,199],"inducing":[33,43,55,83,114,200],"epitopes":[34,38,84,115],"are":[35,142,210],"specific":[36],"antigenic":[37],"capable":[39],"of":[40,46,62,174,180,198],"stimulating":[41],"or":[42],"production":[45],"cells.":[49],"By":[50],"identifying":[51],"studying":[53],"epitopes,":[56],"we":[57,100],"gain":[58],"better":[60],"understanding":[61],"their":[63],"relevance":[64],"diseases,":[66],"offering":[67],"novel":[68,103],"targets":[69],"intervention":[71],"strategies":[72],"for":[73,90,111],"drug":[74],"development":[75],"treatment.":[77],"Furthermore,":[78],"in-depth":[79],"research":[80,209],"on":[81,108],"provides":[85],"important":[86],"insights":[87],"guidance":[89],"personalized":[91],"medicine":[92],"precision":[94],"therapy.":[96],"In":[97,130],"this":[98,208],"study,":[99],"propose":[101],"deep":[104],"learning":[105],"model":[106,118,185],"based":[107],"multi-feature":[109],"fusion":[110,173],"predicting":[112],"(TNFIPs-Net).":[116],"Our":[117],"utilizes":[119],"dual-branch":[121],"architecture":[122],"guided":[123],"by":[124],"adaptive":[125,135],"features":[126,138,141,165,176],"hand-crafted":[128,140],"features.":[129],"encoder":[132],"layers,":[133],"word":[136],"embedding":[137],"separately":[143],"input":[144],"different":[146],"encoders":[147],"(transformer":[148],"BiGRU,":[150],"respectively).":[151],"Finally,":[152],"an":[153],"attention":[154],"mechanism":[155,182],"employed":[157],"output":[160],"layer":[161],"fuse":[163],"from":[166],"both":[167],"branches.":[168],"Through":[169],"experimental":[170],"validation,":[171],"multiple":[175],"use":[179],"self-attention":[181],"enable":[183],"capture":[187],"complex":[188],"feature":[189],"information":[190],"more":[191],"effectively,":[192],"thereby":[193],"improving":[194],"predictive":[196],"performance":[197],"epitopes.":[201],"The":[202],"datasets":[203],"code":[205],"used":[206],"available":[211],"at":[212],"https://github.com/yujiexu321/TNFIPs-Net.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-21T06:30:42.041108","created_date":"2025-10-10T00:00:00"}