{"id":"https://openalex.org/W3202423783","doi":"https://doi.org/10.1371/journal.pcbi.1009465","title":"DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment","display_name":"DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment","publication_year":2021,"publication_date":"2021-10-05","ids":{"openalex":"https://openalex.org/W3202423783","doi":"https://doi.org/10.1371/journal.pcbi.1009465","mag":"3202423783","pmid":"https://pubmed.ncbi.nlm.nih.gov/34610009"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pcbi.1009465","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1009465","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009465&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009465&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033647731","display_name":"Ramzan Umarov","orcid":"https://orcid.org/0000-0003-3477-7101"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ramzan Umarov","raw_affiliation_strings":["Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3477-7101","affiliations":[{"raw_affiliation_string":"Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100345753","display_name":"Yu Li","orcid":"https://orcid.org/0000-0002-3664-6722"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yu Li","raw_affiliation_strings":["Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong, People's Republic of China"],"raw_orcid":"https://orcid.org/0000-0002-3664-6722","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong, People's Republic of China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038145868","display_name":"Erik Arner","orcid":"https://orcid.org/0000-0003-1225-4908"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]},{"id":"https://openalex.org/I4210150921","display_name":"RIKEN Center for Integrative Medical Sciences","ror":"https://ror.org/04mb6s476","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210150921"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Erik Arner","raw_affiliation_strings":["Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan","Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan"],"raw_orcid":"https://orcid.org/0000-0003-1225-4908","affiliations":[{"raw_affiliation_string":"Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]},{"raw_affiliation_string":"Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210150921"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033647731","https://openalex.org/A5038145868"],"corresponding_institution_ids":["https://openalex.org/I113306721","https://openalex.org/I4210150921"],"apc_list":{"value":2655,"currency":"USD","value_usd":2655},"apc_paid":{"value":2655,"currency":"USD","value_usd":2655},"fwci":1.9786,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.88402909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"17","issue":"10","first_page":"e1009465","last_page":"e1009465"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9983999729156494,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9945999979972839,"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/autoencoder","display_name":"Autoencoder","score":0.8181400299072266},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.6096794009208679},{"id":"https://openalex.org/keywords/cell-type","display_name":"Cell type","score":0.5028991103172302},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4874406158924103},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.48450323939323425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4297231137752533},{"id":"https://openalex.org/keywords/drug-response","display_name":"Drug response","score":0.4274212419986725},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4141653776168823},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41254982352256775},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41084861755371094},{"id":"https://openalex.org/keywords/cell","display_name":"Cell","score":0.3900730311870575},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.29924654960632324},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.16704821586608887},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.16198194026947021}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8181400299072266},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.6096794009208679},{"id":"https://openalex.org/C189014844","wikidata":"https://www.wikidata.org/wiki/Q189118","display_name":"Cell type","level":3,"score":0.5028991103172302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4874406158924103},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.48450323939323425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4297231137752533},{"id":"https://openalex.org/C2994119904","wikidata":"https://www.wikidata.org/wiki/Q1251001","display_name":"Drug response","level":3,"score":0.4274212419986725},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4141653776168823},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41254982352256775},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41084861755371094},{"id":"https://openalex.org/C1491633281","wikidata":"https://www.wikidata.org/wiki/Q7868","display_name":"Cell","level":2,"score":0.3900730311870575},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.29924654960632324},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.16704821586608887},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.16198194026947021},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069558","descriptor_name":"Unsupervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069558","descriptor_name":"Unsupervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069558","descriptor_name":"Unsupervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000078722","descriptor_name":"PC-3 Cells","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000078722","descriptor_name":"PC-3 Cells","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000078722","descriptor_name":"PC-3 Cells","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000970","descriptor_name":"Antineoplastic Agents","qualifier_ui":"Q000494","qualifier_name":"pharmacology","is_major_topic":false},{"descriptor_ui":"D000970","descriptor_name":"Antineoplastic Agents","qualifier_ui":"Q000494","qualifier_name":"pharmacology","is_major_topic":false},{"descriptor_ui":"D000970","descriptor_name":"Antineoplastic Agents","qualifier_ui":"Q000494","qualifier_name":"pharmacology","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D055785","descriptor_name":"Gene Knockdown Techniques","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D055785","descriptor_name":"Gene Knockdown Techniques","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D055785","descriptor_name":"Gene Knockdown Techniques","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059467","descriptor_name":"Transcriptome","qualifier_ui":"Q000187","qualifier_name":"drug effects","is_major_topic":false},{"descriptor_ui":"D059467","descriptor_name":"Transcriptome","qualifier_ui":"Q000187","qualifier_name":"drug effects","is_major_topic":false},{"descriptor_ui":"D059467","descriptor_name":"Transcriptome","qualifier_ui":"Q000187","qualifier_name":"drug effects","is_major_topic":false},{"descriptor_ui":"D059467","descriptor_name":"Transcriptome","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":false},{"descriptor_ui":"D059467","descriptor_name":"Transcriptome","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":false},{"descriptor_ui":"D059467","descriptor_name":"Transcriptome","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":false},{"descriptor_ui":"D061986","descriptor_name":"MCF-7 Cells","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D061986","descriptor_name":"MCF-7 Cells","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D061986","descriptor_name":"MCF-7 Cells","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1371/journal.pcbi.1009465","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1009465","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009465&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},{"id":"pmid:34610009","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34610009","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":"PLoS computational biology","raw_type":null},{"id":"pmh:oai:doaj.org/article:1e922526e25248f6b5086385720b05c1","is_oa":true,"landing_page_url":"https://doaj.org/article/1e922526e25248f6b5086385720b05c1","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS Computational Biology, Vol 17, Iss 10, p e1009465 (2021)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:8519465","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8519465","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":"PLoS Comput Biol","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1371/journal.pcbi.1009465","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1009465","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009465&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1992407562","https://openalex.org/W2025768430","https://openalex.org/W2094791588","https://openalex.org/W2121604817","https://openalex.org/W2225391592","https://openalex.org/W2259632819","https://openalex.org/W2552465644","https://openalex.org/W2555803041","https://openalex.org/W2560042709","https://openalex.org/W2612467560","https://openalex.org/W2735897797","https://openalex.org/W2745986812","https://openalex.org/W2765710211","https://openalex.org/W2769760195","https://openalex.org/W2770808765","https://openalex.org/W2896822135","https://openalex.org/W2898097650","https://openalex.org/W2898718502","https://openalex.org/W2900044275","https://openalex.org/W2921591500","https://openalex.org/W2943555376","https://openalex.org/W2951381561","https://openalex.org/W2964121744","https://openalex.org/W2965552103","https://openalex.org/W2976451995","https://openalex.org/W2978541146","https://openalex.org/W2991297228","https://openalex.org/W3120224790","https://openalex.org/W6713134421","https://openalex.org/W7075682736"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2752972570","https://openalex.org/W4297051394","https://openalex.org/W2734887215","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4365790226"],"abstract_inverted_index":{"Drug":[0],"treatment":[1],"induces":[2],"cell":[3,17,48,60,91,102],"type":[4,49],"specific":[5],"transcriptional":[6,27,44,70],"programs,":[7],"and":[8,16,80,120],"as":[9,135],"the":[10,20,26,42,55,63,87,98,121,129,141,146],"number":[11],"of":[12,14,69,140],"combinations":[13],"drugs":[15,106],"types":[18],"grows,":[19],"cost":[21],"for":[22,40,127],"exhaustive":[23],"screens":[24],"measuring":[25],"drug":[28,51,56,71,109],"response":[29,57],"becomes":[30],"intractable.":[31],"We":[32],"developed":[33],"DeepCellState,":[34],"a":[35,47,66,158],"deep":[36,82],"learning":[37,83],"autoencoder-based":[38],"framework,":[39],"predicting":[41,128],"induced":[43],"state":[45],"in":[46,58,157],"after":[50],"treatment,":[52],"based":[53],"on":[54,65],"another":[59],"type.":[61],"Training":[62],"method":[64,88],"large":[67],"collection":[68],"perturbation":[72],"profiles,":[73],"prediction":[74],"accuracy":[75,95],"improves":[76],"significantly":[77],"over":[78],"baseline":[79],"alternative":[81],"approaches":[84],"when":[85,96],"applying":[86],"to":[89,100,151],"two":[90],"types,":[92],"with":[93,105,117],"improved":[94],"generalizing":[97],"framework":[99,123],"additional":[101],"types.":[103],"Treatments":[104],"or":[107],"whole":[108],"families":[110],"not":[111],"seen":[112],"during":[113],"training":[114],"are":[115],"predicted":[116],"similar":[118],"accuracy,":[119],"same":[122],"can":[124],"be":[125],"used":[126],"results":[130],"from":[131],"other":[132],"interventions,":[133],"such":[134],"gene":[136],"knock-downs.":[137],"Finally,":[138],"analysis":[139],"trained":[142],"model":[143],"shows":[144],"that":[145],"internal":[147],"representation":[148],"is":[149],"able":[150],"learn":[152],"regulatory":[153],"relationships":[154],"between":[155],"genes":[156],"fully":[159],"data-driven":[160],"manner.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-03T09:05:47.796612","created_date":"2025-10-10T00:00:00"}
