{"id":"https://openalex.org/W7167689314","doi":"https://doi.org/10.48550/arxiv.2607.05846","title":"AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking","display_name":"AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking","publication_year":2026,"publication_date":"2026-07-07","ids":{"openalex":"https://openalex.org/W7167689314","doi":"https://doi.org/10.48550/arxiv.2607.05846"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.05846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.05846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.05846","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140239862","display_name":"Zhiyuan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140260107","display_name":"Jing Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140226973","display_name":"Junzhe Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Junzhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037657820","display_name":"Yueyang Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yueyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140281592","display_name":"Xinyi Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xinyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140250820","display_name":"Zhaoyang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140291824","display_name":"Feng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11016","display_name":"Monoclonal and Polyclonal Antibodies Research","score":0.8021000027656555,"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"}},"topics":[{"id":"https://openalex.org/T11016","display_name":"Monoclonal and Polyclonal Antibodies Research","score":0.8021000027656555,"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/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.15809999406337738,"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/T11124","display_name":"Protein purification and stability","score":0.008999999612569809,"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/ranking","display_name":"Ranking (information retrieval)","score":0.8080000281333923},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6359000205993652},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5116999745368958},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5040000081062317},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4799000024795532},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4772999882698059},{"id":"https://openalex.org/keywords/ranking-svm","display_name":"Ranking SVM","score":0.4487000107765198},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4108999967575073}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.8080000281333923},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6359000205993652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5867999792098999},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.554099977016449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.53329998254776},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5116999745368958},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4799000024795532},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4772999882698059},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.4487000107765198},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.34850001335144043},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C2781377902","wikidata":"https://www.wikidata.org/wiki/Q21418769","display_name":"Affinity maturation","level":3,"score":0.3124000132083893},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2847000062465668},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C179247698","wikidata":"https://www.wikidata.org/wiki/Q382799","display_name":"Affinity chromatography","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.05846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.05846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.05846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.05846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6614902019500732,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"ranking":[1,71,98,126,161,225],"of":[2,48,80,89,177,206],"antibody":[3,14,96,114,214],"candidates":[4],"according":[5],"to":[6,36,68,94,140],"their":[7,34],"binding":[8,39],"affinity":[9,20,51,76,97,115,199,215],"is":[10,59,129,227],"essential":[11],"for":[12,112,144,212],"therapeutic":[13],"discovery.":[15],"However,":[16],"existing":[17,66,160],"methods":[18],"treat":[19],"comparisons":[21,52,67],"independently":[22],"and":[23,128,168,190,197],"ignore":[24],"the":[25,61,87,138,152,175,186,204],"contextual":[26,178],"information":[27],"encoded":[28],"in":[29,218],"other":[30],"labeled":[31,83],"comparisons,":[32],"limiting":[33],"ability":[35],"capture":[37],"antigen-specific":[38,70,113,213],"landscapes.":[40],"For":[41],"many":[42],"target":[43,187],"antigens,":[44],"a":[45,119,124,222],"small":[46],"number":[47],"experimentally":[49],"characterized":[50],"are":[53],"often":[54],"available.":[55],"An":[56],"important":[57],"question":[58],"whether":[60],"model":[62,139],"can":[63],"exploit":[64],"these":[65],"infer":[69],"patterns":[72],"that":[73,136,156,174],"facilitate":[74],"subsequent":[75],"ranking.":[77,116],"This":[78],"form":[79],"learning":[81],"from":[82,99],"demonstrations":[84,143,179],"closely":[85],"resembles":[86],"paradigm":[88,211],"In-Context":[90],"Learning,":[91],"motivating":[92],"us":[93],"revisit":[95],"an":[100,109,132,209],"ICL":[101,110,207],"perspective.":[102],"To":[103],"this":[104],"end,":[105],"we":[106],"propose":[107],"AbICL,":[108],"framework":[111],"AbICL":[117,157],"combines":[118],"pretrained":[120],"structural":[121],"encoder":[122],"with":[123,131],"context":[125],"head":[127],"trained":[130],"episodic":[133],"meta-training":[134],"strategy":[135],"enables":[137],"leverage":[141],"support":[142],"test-time":[145],"adaptation":[146],"without":[147],"gradient":[148],"updates.":[149],"Experiments":[150],"on":[151,181],"AbRank":[153],"benchmark":[154],"demonstrate":[155],"consistently":[158],"outperforms":[159],"baselines":[162],"across":[163],"almost":[164],"all":[165],"data":[166],"splits":[167],"evaluation":[169],"benchmarks.":[170],"Further":[171],"analysis":[172],"shows":[173],"value":[176],"depends":[180],"how":[182],"well":[183],"they":[184],"match":[185],"inference":[188],"task,":[189],"becomes":[191],"increasingly":[192],"pronounced":[193],"under":[194],"distribution":[195],"shift":[196],"fine-grained":[198],"discrimination.":[200],"These":[201],"findings":[202],"highlight":[203],"potential":[205],"as":[208],"effective":[210],"ranking,":[216],"particularly":[217],"challenging":[219],"settings":[220],"where":[221],"single":[223],"global":[224],"function":[226],"insufficient.":[228]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-09T00:00:00"}
