{"id":"https://openalex.org/W7126066136","doi":"https://doi.org/10.1109/bibm66473.2025.11356589","title":"Predicting Nanobody Paratope via Fused Attention Mechanism and Distance-Guided Interaction Learning","display_name":"Predicting Nanobody Paratope via Fused Attention Mechanism and Distance-Guided Interaction Learning","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126066136","doi":"https://doi.org/10.1109/bibm66473.2025.11356589"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5124238229","display_name":"Zhanhua Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhanhua Lu","raw_affiliation_strings":["Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079"],"affiliations":[{"raw_affiliation_string":"Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062662868","display_name":"J. Joshua Yang","orcid":"https://orcid.org/0000-0001-8242-7531"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiatai Yang","raw_affiliation_strings":["Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079"],"affiliations":[{"raw_affiliation_string":"Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121420414","display_name":"Weizhong Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weizhong Zhao","raw_affiliation_strings":["Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079"],"affiliations":[{"raw_affiliation_string":"Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003550654","display_name":"Xingpeng Jiang","orcid":"https://orcid.org/0000-0002-8848-9300"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingpeng Jiang","raw_affiliation_strings":["Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079"],"affiliations":[{"raw_affiliation_string":"Central China Normal University,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan,Hubei,PR China,430079","institution_ids":["https://openalex.org/I40963666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5124238229"],"corresponding_institution_ids":["https://openalex.org/I40963666"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65178237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"166","last_page":"171"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.49950000643730164,"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":0.49950000643730164,"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/T11016","display_name":"Monoclonal and Polyclonal Antibodies Research","score":0.48240000009536743,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10580","display_name":"Immunotherapy and Immune Responses","score":0.00430000014603138,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/paratope","display_name":"Paratope","score":0.9660999774932861},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.6252999901771545},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6200000047683716},{"id":"https://openalex.org/keywords/interaction-information","display_name":"Interaction information","score":0.5037999749183655},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.49709999561309814},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4399000108242035},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3855000138282776}],"concepts":[{"id":"https://openalex.org/C9086966","wikidata":"https://www.wikidata.org/wiki/Q489135","display_name":"Paratope","level":4,"score":0.9660999774932861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7648000121116638},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.6252999901771545},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6200000047683716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5440000295639038},{"id":"https://openalex.org/C38764148","wikidata":"https://www.wikidata.org/wiki/Q17098245","display_name":"Interaction information","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.49709999561309814},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4399000108242035},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3855000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3659000098705292},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.31360000371932983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C11804247","wikidata":"https://www.wikidata.org/wiki/Q896177","display_name":"Protein\u2013protein interaction","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/4","score":0.4775438606739044,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G1247235041","display_name":null,"funder_award_id":"62372205,62472192","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2801965704","https://openalex.org/W2808491442","https://openalex.org/W2891129333","https://openalex.org/W3016492430","https://openalex.org/W3034552520","https://openalex.org/W3123025390","https://openalex.org/W3138783506","https://openalex.org/W3177500196","https://openalex.org/W3207204660","https://openalex.org/W4220694779","https://openalex.org/W4297198862","https://openalex.org/W4304820621","https://openalex.org/W4308825561","https://openalex.org/W4327550249","https://openalex.org/W4367845152","https://openalex.org/W4393044531","https://openalex.org/W4403784265","https://openalex.org/W4405104445","https://openalex.org/W4407167422","https://openalex.org/W4408546009","https://openalex.org/W4410359893"],"related_works":[],"abstract_inverted_index":{"The":[0],"task":[1],"of":[2,27,61,118,134,140],"nanobody":[3,13,30,87,125,161,195],"paratope":[4,88,126,196],"prediction":[5,89,139,197],"aims":[6],"to":[7,17,104,114,155,188,194,218,239],"identify":[8],"the":[9,25,71,116,123,132,138,149,186,216,226],"residues":[10],"on":[11,46,143,190],"a":[12,85,93],"that":[14,91,177,233],"specifically":[15],"bind":[16],"an":[18,28],"antigen,":[19],"which":[20],"is":[21,128],"crucial":[22,74],"for":[23,35,75],"understanding":[24],"mechanisms":[26],"effective":[29],"agent,":[31],"providing":[32],"significant":[33],"implications":[34],"drug":[36],"development":[37],"accordingly.":[38],"Although":[39],"various":[40],"methods":[41,50],"have":[42],"been":[43],"proposed":[44],"based":[45],"different":[47],"strategies,":[48],"existing":[49,124,240],"still":[51],"face":[52],"several":[53],"challenges:":[54],"overlooking":[55],"nanobody-specific":[56,76],"structural":[57],"characteristics,":[58],"neglecting":[59],"optimization":[60],"high-dimensional":[62],"features":[63,192],"extracted":[64],"from":[65],"antibody":[66,151],"language":[67,152],"models,":[68],"and":[69,100,109,169,180,198,208,224],"disregarding":[70],"antigen":[72],"information":[73],"binding.":[77],"To":[78],"address":[79],"these":[80],"challenges,":[81],"we":[82],"propose":[83],"NanoFADIL,":[84],"novel":[86],"model":[90,153,187,217],"employs":[92],"fused":[94,174],"attention":[95,99,103,175],"mechanism":[96,176],"combining":[97],"channel":[98],"one-dimensional":[101,181],"spatial":[102,182],"optimize":[105],"feature":[106,158,167],"representations":[107,162],"adaptively,":[108],"uses":[110],"residue-level":[111,204],"distance":[112],"supervision":[113,213],"guide":[115],"learning":[117],"nanobody-antigen":[119,221],"interaction":[120,222],"patterns.":[121],"Specifically,":[122],"dataset":[127],"augmented":[129],"by":[130,148,172],"incorporating":[131],"sequences":[133],"corresponding":[135],"antigens,":[136],"allowing":[137],"antigen-specific":[141],"paratopes":[142],"nanobodies.":[144],"Nanobodies":[145],"are":[146,163,210],"encoded":[147],"pretrained":[150],"IgT5":[154],"derive":[156],"meaningful":[157],"representations.":[159],"High-dimensional":[160],"adaptively":[164],"reweighted":[165],"across":[166],"channels":[168],"residue":[170],"positions":[171],"our":[173],"combines":[178],"channel-wise":[179],"attention,":[183],"thereby":[184],"enabling":[185],"focus":[189],"informative":[191],"relevant":[193],"contextually":[199],"important":[200],"residues.":[201],"During":[202],"training,":[203],"distances":[205],"between":[206],"nanobodies":[207],"antigens":[209],"transformed":[211],"into":[212],"signals,":[214],"guiding":[215],"learn":[219],"meaning-ful":[220],"patterns":[223],"enhancing":[225],"model's":[227],"predictive":[228],"capability.":[229],"Experimental":[230],"results":[231],"demonstrate":[232],"NanoFADIL":[234],"achieves":[235],"superior":[236],"performance":[237],"compared":[238],"methods.":[241]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-30T00:00:00"}
