{"id":"https://openalex.org/W7140199814","doi":"https://doi.org/10.48550/arxiv.2603.20825","title":"Cross-Granularity Representations for Biological Sequences: Insights from ESM and BiGCARP","display_name":"Cross-Granularity Representations for Biological Sequences: Insights from ESM and BiGCARP","publication_year":2026,"publication_date":"2026-03-21","ids":{"openalex":"https://openalex.org/W7140199814","doi":"https://doi.org/10.48550/arxiv.2603.20825"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20825","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20825","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2603.20825","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiao, Hanlin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Hanlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Breitling, Rainer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Breitling, Rainer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Takano, Eriko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takano, Eriko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"\u00c1lvarez, Mauricio A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00c1lvarez, Mauricio A.","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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.2685000002384186,"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":0.2685000002384186,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.1120000034570694,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.08569999784231186,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8608999848365784},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.7127000093460083},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6355000138282776},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5839999914169312},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5443000197410583},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5295000076293945},{"id":"https://openalex.org/keywords/biological-data","display_name":"Biological data","score":0.40369999408721924}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8608999848365784},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.7127000093460083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6575000286102295},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6355000138282776},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5839999914169312},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5295000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48500001430511475},{"id":"https://openalex.org/C201797286","wikidata":"https://www.wikidata.org/wiki/Q4914986","display_name":"Biological data","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.385699987411499},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33660000562667847},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3043000102043152},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2590999901294708}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20825","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20825","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2603.20825","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20825","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":0.6788971424102783,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,100,139],"general-purpose":[3],"foundation":[4,161],"models":[5,53],"have":[6],"stimulated":[7],"the":[8,47,115,155],"development":[9],"of":[10,49,58,83,114,159],"large":[11],"biological":[12,24,129,160],"sequence":[13],"models.":[14,162],"While":[15],"natural":[16],"language":[17,74],"shows":[18],"symbolic":[19],"granularity":[20,28],"(characters,":[21],"words,":[22],"sentences),":[23],"sequences":[25],"exhibit":[26],"hierarchical":[27],"whose":[29],"levels":[30],"(nucleotides,":[31],"amino":[32,71],"acids,":[33],"protein":[34,73],"domains,":[35],"genes)":[36],"further":[37],"encode":[38,127],"biologically":[39],"functional":[40],"information.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45,86,120],"investigate":[46],"integration":[48,147],"cross-granularity":[50,146],"knowledge":[51],"from":[52],"through":[54],"a":[55,60,81,90,108,149],"case":[56],"study":[57],"BiGCARP,":[59,101],"Pfam":[61],"domain-level":[62],"model":[63],"for":[64,152],"biosynthetic":[65],"gene":[66],"clusters,":[67],"and":[68,80,102,111,131,157],"ESM,":[69],"an":[70],"acid-level":[72],"model.":[75],"Using":[76],"representation":[77,113],"analysis":[78],"tools":[79],"set":[82],"probe":[84],"tasks,":[85],"first":[87],"explain":[88],"why":[89],"straightforward":[91],"cross-model":[92],"embedding":[93],"initialization":[94],"fails":[95],"to":[96],"improve":[97],"downstream":[98],"performance":[99,137,156],"show":[103],"that":[104,122,132],"deeper-layer":[105],"embeddings":[106],"capture":[107],"more":[109],"contextual":[110],"faithful":[112],"model's":[116],"learned":[117],"knowledge.":[118],"Furthermore,":[119],"demonstrate":[121],"representations":[123],"at":[124],"different":[125],"granularities":[126],"complementary":[128],"knowledge,":[130],"combining":[133],"them":[134],"yields":[135],"measurable":[136],"gains":[138],"intermediate-level":[140],"prediction":[141],"tasks.":[142],"Our":[143],"findings":[144],"highlight":[145],"as":[148],"promising":[150],"strategy":[151],"improving":[153],"both":[154],"interpretability":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}
