{"id":"https://openalex.org/W4226191767","doi":"https://doi.org/10.1186/s12859-022-04642-w","title":"Investigation of improving the pre-training and fine-tuning of BERT model for biomedical relation extraction","display_name":"Investigation of improving the pre-training and fine-tuning of BERT model for biomedical relation extraction","publication_year":2022,"publication_date":"2022-04-04","ids":{"openalex":"https://openalex.org/W4226191767","doi":"https://doi.org/10.1186/s12859-022-04642-w","pmid":"https://pubmed.ncbi.nlm.nih.gov/35379166"},"language":"en","primary_location":{"id":"doi:10.1186/s12859-022-04642-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-022-04642-w","pdf_url":"https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04642-w","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04642-w","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101679139","display_name":"Peng Su","orcid":"https://orcid.org/0000-0003-3592-6762"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Su","raw_affiliation_strings":["Department of Computer and Information Science, Biomedical Text Mining Lab, University of Delaware, Newark, USA. psu@udel.edu","Department of Computer and Information Science, Biomedical Text Mining Lab, University of Delaware, Newark, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, Biomedical Text Mining Lab, University of Delaware, Newark, USA. psu@udel.edu","institution_ids":["https://openalex.org/I86501945"]},{"raw_affiliation_string":"Department of Computer and Information Science, Biomedical Text Mining Lab, University of Delaware, Newark, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027009667","display_name":"K. Vijay\u2010Shanker","orcid":"https://orcid.org/0000-0003-0958-3073"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K. Vijay-Shanker","raw_affiliation_strings":["Department of Computer and Information Science, Biomedical Text Mining Lab, University of Delaware, Newark, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, Biomedical Text Mining Lab, University of Delaware, Newark, USA","institution_ids":["https://openalex.org/I86501945"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86501945"],"apc_list":{"value":2790,"currency":"USD","value_usd":2790},"apc_paid":{"value":2790,"currency":"USD","value_usd":2790},"fwci":3.9957,"has_fulltext":true,"cited_by_count":56,"citation_normalized_percentile":{"value":0.95415857,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"23","issue":"1","first_page":"120","last_page":"120"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.4092999994754791,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.4092999994754791,"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/T10028","display_name":"Topic Modeling","score":0.23639999330043793,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.15399999916553497,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8623945713043213},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7782280445098877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5708486437797546},{"id":"https://openalex.org/keywords/biomedical-text-mining","display_name":"Biomedical text mining","score":0.5687435269355774},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5495832562446594},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5458834171295166},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5432887673377991},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5194175243377686},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.519108772277832},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4818888306617737},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4717462956905365},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4203081727027893},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41350027918815613},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.28988951444625854},{"id":"https://openalex.org/keywords/text-mining","display_name":"Text mining","score":0.08576062321662903}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8623945713043213},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7782280445098877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5708486437797546},{"id":"https://openalex.org/C165141518","wikidata":"https://www.wikidata.org/wiki/Q4915126","display_name":"Biomedical text mining","level":3,"score":0.5687435269355774},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5495832562446594},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5458834171295166},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5432887673377991},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5194175243377686},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.519108772277832},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4818888306617737},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4717462956905365},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4203081727027893},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41350027918815613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28988951444625854},{"id":"https://openalex.org/C71472368","wikidata":"https://www.wikidata.org/wiki/Q676880","display_name":"Text mining","level":2,"score":0.08576062321662903},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011642","descriptor_name":"Publications","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011642","descriptor_name":"Publications","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011642","descriptor_name":"Publications","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011642","descriptor_name":"Publications","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D035843","descriptor_name":"Biomedical Research","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D035843","descriptor_name":"Biomedical Research","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D035843","descriptor_name":"Biomedical Research","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D035843","descriptor_name":"Biomedical Research","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true}],"locations_count":4,"locations":[{"id":"doi:10.1186/s12859-022-04642-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-022-04642-w","pdf_url":"https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04642-w","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},{"id":"pmid:35379166","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35379166","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC bioinformatics","raw_type":null},{"id":"pmh:oai:doaj.org/article:cd549500855e4984ad0ba4733ba62c36","is_oa":false,"landing_page_url":"https://doaj.org/article/cd549500855e4984ad0ba4733ba62c36","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics, Vol 23, Iss 1, Pp 1-20 (2022)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:8978438","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8978438","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s12859-022-04642-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-022-04642-w","pdf_url":"https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04642-w","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7900000214576721}],"awards":[{"id":"https://openalex.org/G2999675554","display_name":null,"funder_award_id":"U01GM125267","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3283048307","display_name":null,"funder_award_id":"U01 GM125267","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4226191767.pdf","grobid_xml":"https://content.openalex.org/works/W4226191767.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1566256432","https://openalex.org/W1574174742","https://openalex.org/W1587191403","https://openalex.org/W1964162497","https://openalex.org/W1964670939","https://openalex.org/W1981276685","https://openalex.org/W2064030835","https://openalex.org/W2064675550","https://openalex.org/W2074523946","https://openalex.org/W2098162425","https://openalex.org/W2120814856","https://openalex.org/W2166474856","https://openalex.org/W2170189740","https://openalex.org/W2396881363","https://openalex.org/W2801930304","https://openalex.org/W2911489562","https://openalex.org/W2946417913","https://openalex.org/W2960293113","https://openalex.org/W2962739339","https://openalex.org/W2964026782","https://openalex.org/W2965166224","https://openalex.org/W2970641574","https://openalex.org/W2971258845","https://openalex.org/W3034238904","https://openalex.org/W3046375318","https://openalex.org/W3127429900","https://openalex.org/W3165163830","https://openalex.org/W4205773061","https://openalex.org/W4365511667","https://openalex.org/W6600223405","https://openalex.org/W6685158001","https://openalex.org/W6702248584","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W2994720652","https://openalex.org/W2915573705","https://openalex.org/W2057069926","https://openalex.org/W2531741693","https://openalex.org/W3111301126","https://openalex.org/W3004540147","https://openalex.org/W3045642779","https://openalex.org/W2572241437","https://openalex.org/W2963862093","https://openalex.org/W2527712604"],"abstract_inverted_index":{"BACKGROUND:":[0],"Recently,":[1],"automatically":[2],"extracting":[3],"biomedical":[4,12,20,27,39],"relations":[5],"has":[6],"been":[7],"a":[8],"significant":[9],"subject":[10],"in":[11,61,104,194],"research":[13],"due":[14],"to":[15,25,52,85,99,110,200],"the":[16,23,26,29,50,54,63,72,87,101,105,128,134,141,178,192,195,202,206,214,220],"rapid":[17],"growth":[18],"of":[19,68,79,108,150,198,208,216],"literature.":[21],"Since":[22],"adaptation":[24,81],"domain,":[28],"transformer-based":[30],"BERT":[31,55,80,109,129,143,179,199,217],"models":[32,144],"have":[33],"produced":[34],"leading":[35],"results":[36,117],"on":[37,82,152,162,173,182,219],"many":[38],"natural":[40],"language":[41],"processing":[42],"tasks.":[43,155,223],"In":[44,71],"this":[45],"work,":[46],"we":[47,75,96],"will":[48],"explore":[49],"approaches":[51,121,211],"improve":[53,111,127],"model":[56,130,180,203,218],"for":[57,122],"relation":[58,153,164,221],"extraction":[59,154,165,222],"tasks":[60],"both":[62],"pre-training":[64,73,123,171],"and":[65,92,124,185],"fine-tuning":[66,125,188],"stages":[67],"its":[69,112],"applications.":[70],"stage,":[74],"add":[76],"another":[77],"level":[78],"sub-domain":[83,174],"data":[84,175],"bridge":[86],"gap":[88],"between":[89],"domain":[90],"knowledge":[91,103,193],"task-specific":[93],"knowledge.":[94],"Also,":[95],"propose":[97],"methods":[98],"incorporate":[100],"ignored":[102],"last":[106,196],"layer":[107,197],"fine-tuning.":[113],"RESULTS:":[114],"The":[115,169],"experiment":[116],"demonstrate":[118],"that":[119],"our":[120,138,157,186],"can":[126,176],"performance.":[131,204],"After":[132],"combining":[133],"two":[135,210],"proposed":[136,187],"techniques,":[137],"approach":[139,158],"outperforms":[140],"original":[142],"with":[145],"averaged":[146],"F1":[147],"score":[148],"improvement":[149],"2.1%":[151],"Moreover,":[156],"achieves":[159],"state-of-the-art":[160],"performance":[161,215],"three":[163],"benchmark":[166],"datasets.":[167],"CONCLUSIONS:":[168],"extra":[170],"step":[172],"help":[177],"generalization":[181],"specific":[183],"tasks,":[184],"mechanism":[189],"could":[190],"utilize":[191],"boost":[201],"Furthermore,":[205],"combination":[207],"these":[209],"further":[212],"improves":[213]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
