{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T13:19:33Z","timestamp":1774617573820,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03394-4","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:56:28Z","timestamp":1771613788000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Therapeutic monoclonal antibodies repurposing in oncology via IMGT\/mAb-KG embeddings"],"prefix":"10.1186","volume":"26","author":[{"given":"Gaoussou","family":"Sanou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taciana","family":"Manso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantin","family":"Todorov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V\u00e9ronique","family":"Giudicelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrice","family":"Duroux","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sofia","family":"Kossida","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"3394_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clinicians. 2024;74:229\u201363.","journal-title":"CA: Cancer J Clinicians"},{"key":"3394_CR2","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clinicians. 2021;71:209\u201349. https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.3322\/caac.21660.","journal-title":"CA: Cancer J Clinicians"},{"key":"3394_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.47611\/jsrhs.v12i2.4236","volume":"12","author":"S Srirapu","year":"2023","unstructured":"Srirapu S. Monoclonal antibodies and their applications in cancer. J Student Res. 2023;12:1\u20137.","journal-title":"J Student Res"},{"key":"3394_CR4","doi-asserted-by":"crossref","unstructured":"Castelli MS, McGonigle P, Hornby PJ. The pharmacology and therapeutic applications of monoclonal antibodies. Pharmacol Res Perspectives. 2019;7:e00535.","DOI":"10.1002\/prp2.535"},{"key":"3394_CR5","doi-asserted-by":"crossref","unstructured":"Dos Santos ML, Quintilio W, Manieri TM, Tsuruta LR, Moro AM. Advances and challenges in therapeutic monoclonal antibodies drug development. 2018.","DOI":"10.1590\/s2175-97902018000001007"},{"key":"3394_CR6","doi-asserted-by":"publisher","unstructured":"Ecker DM, Jones SD, Levine HL. The therapeutic monoclonal antibody market. 2015. https:\/\/doi.org\/10.4161\/19420862.2015.989042.","DOI":"10.4161\/19420862.2015.989042"},{"key":"3394_CR7","doi-asserted-by":"crossref","unstructured":"Lu RM, et al. Development of therapeutic antibodies for the treatment of diseases. J Biomed Sci 2020;27(1):1\u201330. https:\/\/jbiomedsci.biomedcentral.com\/articles\/10.1186\/s12929-019-0592-z.","DOI":"10.1186\/s12929-019-0592-z"},{"key":"3394_CR8","doi-asserted-by":"crossref","unstructured":"Golbeck J, et al. The National cancer Institute\u2019s thesaurus and ontology. SSRN Electron J. 2003. https:\/\/papers.ssrn.com\/abstract=3199007.","DOI":"10.2139\/ssrn.3199007"},{"key":"3394_CR9","doi-asserted-by":"crossref","unstructured":"Raybould MI, et al. Thera-SAbDab: the therapeutic structural antibody database. Nucleic Acids Res. 2020;48:D383\u201388.","DOI":"10.1093\/nar\/gkz827"},{"key":"3394_CR10","doi-asserted-by":"crossref","unstructured":"Abanades B, et al. The patent and literature antibody database (PLAbDab): an evolving reference set of functionally diverse, literatureannotated antibody sequences and structures. bioRxiv. 2023;2023(7.15.549143). https:\/\/www.biorxiv.org\/content\/10.1101\/2023.07.15.549143v1.","DOI":"10.1101\/2023.07.15.549143"},{"key":"3394_CR11","unstructured":"C. P, AndGinestoux C., Y. W, Ehrenmann DP, M.-P. L. IMGT\/mAb- DB: the IMGT\u00ae database for therapeutic monoclonal antibodies. 2010. http:\/\/www.imgt.org."},{"key":"3394_CR12","doi-asserted-by":"publisher","first-page":"1393839","DOI":"10.3389\/fimmu.2024.1393839","volume":"15","author":"G Sanou","year":"2024","unstructured":"Sanou G, et al. IMGT\/mAb-KG: the knowledge graph for therapeutic monoclonal antibodies. Front Immunol. 2024;15:1393839. https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2024.1393839\/full.","journal-title":"Front Immunol"},{"key":"3394_CR13","doi-asserted-by":"crossref","unstructured":"Mohamed SK, Nounu A, Nov\u00e1\u010dek V. Drug target discovery using knowledge graph embeddings. Proceedings of the ACM Symposium on Applied Computing Part F1477. 2019, 11\u201318.","DOI":"10.1145\/3297280.3297282"},{"key":"3394_CR14","doi-asserted-by":"publisher","first-page":"59","DOI":"10.12793\/tcp.2019.27.2.59","volume":"27","author":"K Park","year":"2019","unstructured":"Park K. A review of computational drug repurposing. Transl Clin Pharmacol. 2019;27:59\u201363. https:\/\/doi.org\/10.12793\/tcp.2019.27.2.59.","journal-title":"Transl Clin Pharmacol"},{"key":"3394_CR15","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1080\/17460441.2021.1910673","volume":"16","author":"F MacLean","year":"2021","unstructured":"MacLean F. Knowledge graphs and their applications in drug discovery. Expert Opin Drug Discov. 2021;16:1057\u201369. https:\/\/doi.org\/10.1080\/17460441.2021.1910673.","journal-title":"Expert Opin Drug Discov"},{"key":"3394_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-023-02757-0","volume":"10","author":"M Boudin","year":"2023","unstructured":"Boudin M, Diallo G, Dranc\u00e9 M, Mougin F. The OREGANO knowledge graph for computational drug repurposing. Sci Data. 2023;10:1\u201313. https:\/\/www.nature.com\/articles\/s41597-023-02757-0.","journal-title":"Sci Data"},{"key":"3394_CR17","doi-asserted-by":"crossref","unstructured":"Mullard A. New drugs cost US$ 2.6 billion to develop. Nat Publishing Group. 2014.","DOI":"10.1038\/nrd4507"},{"key":"3394_CR18","doi-asserted-by":"crossref","unstructured":"Ghorbanali Z, Zare-Mirakabad F, Akbari M, Salehi N, Masoudi-Nejad A. DrugRep-KG: toward learning a unified latent space for drug repurposing using knowledge graphs. J Chem Inf Modeling. 2023.","DOI":"10.1021\/acs.jcim.2c01291"},{"key":"3394_CR19","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1056\/NEJMoa0809493","volume":"361","author":"SR Cummings","year":"2009","unstructured":"Cummings SR, et al. Denosumab for prevention of fractures in postmenopausal women with osteoporosis. The N Engl J Med. 2009;361:756\u201365. https:\/\/pubmed.ncbi.nlm.nih.gov\/19671655\/.","journal-title":"The N Engl J Med"},{"key":"3394_CR20","doi-asserted-by":"crossref","unstructured":"Bhulani N, et al. A phase 3 study to determine the breast cancer risk reducing effect of denosumab in women carrying a germline BRCA1 mutation (BRCA-P study). J Clin Oncol. 2022;40:TPS10616\u201310616. https:\/\/ascopubs.org\/doi\/10.1200\/JCO.2022.40.16suppl.TPS10616.","DOI":"10.1200\/JCO.2022.40.16_suppl.TPS10616"},{"key":"3394_CR21","doi-asserted-by":"crossref","unstructured":"Gimeno A, et al. The light and dark sides of virtual screening: what is there to know? Int J Mol Sci. 2019;20. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6470506\/.","DOI":"10.3390\/ijms20061375"},{"key":"3394_CR22","doi-asserted-by":"publisher","first-page":"18477","DOI":"10.1073\/pnas.2000585117","volume":"117","author":"YO Adeshina","year":"2020","unstructured":"Adeshina YO, Deeds EJ, Karanicolas J. Machine learning classification can reduce false positives in structure-based virtual screening. Proc The Natl Acad Sci The U States Am. 2020;117:18477\u201388. https:\/\/www.pnas.org\/doi\/abs\/10.1073\/pnas.2000585117.","journal-title":"Proc The Natl Acad Sci The U States Am"},{"key":"3394_CR23","doi-asserted-by":"crossref","unstructured":"Zhang R, et al. Drug repurposing for COVID-19 via knowledge graph completion. J Biomed Inf. 2021;115. http:\/\/arxiv.org\/abs\/2010.0960010.1016\/j.jbi.2021.103696.","DOI":"10.1016\/j.jbi.2021.103696"},{"key":"3394_CR24","doi-asserted-by":"publisher","unstructured":"Islam K, et al. Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding. Sci Rep \u201413. 3643(123). https:\/\/doi.org\/10.1038\/s41598-023-30095-z.","DOI":"10.1038\/s41598-023-30095-z"},{"key":"3394_CR25","doi-asserted-by":"crossref","unstructured":"Grohe MW, N. Graph 2vec, X2vec: towards a theory of vector embeddings of structured data. Proceedings of the ACM SIGACT-SIGMODSIGART Symposium on Principles of Database Systems. 2020 1\u201316.","DOI":"10.1145\/3375395.3387641"},{"key":"3394_CR26","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.jbi.2006.01.003","volume":"39","author":"JA Blake","year":"2006","unstructured":"Blake JA, Bult CJ. Beyond the data deluge: data integration and bioontologies. J Biomed Inf. 2006;39:314\u201320. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046406000190.","journal-title":"J Biomed Inf"},{"key":"3394_CR27","doi-asserted-by":"crossref","unstructured":"Chen Z, et al. A knowledge graph of clinical trials ($$\\mathop {\\mathtt {CTKG}}\\limits$$). Sci Rep. 2022;12:4724. https:\/\/doi.org\/10.1038\/s41598-022-08454-z. https:\/\/www.nature.com\/articles\/s41598-022-08454-z.","DOI":"10.1038\/s41598-022-08454-z"},{"key":"3394_CR28","doi-asserted-by":"publisher","first-page":"100155","DOI":"10.1016\/j.patter.2020.100155","volume":"2","author":"JT Reese","year":"2021","unstructured":"Reese JT, et al. KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response. Patterns. 2021;2:100155. https:\/\/doi.org\/10.1016\/j.patter.2020.100155.","journal-title":"Patterns"},{"key":"3394_CR29","first-page":"628","volume":"13489 LNCS","author":"G Sanou","year":"2022","unstructured":"Sanou G, et al. IMGT-KG: a knowledge graph for immunogenetics. Lect Notes Comput Sci (Incl Subser Lect Notes Artif Intel Lect Notes Bioinf). 2022;13489 LNCS:628\u201342.","journal-title":"Lect Notes Comput Sci (Incl Subser Lect Notes Artif Intel Lect Notes Bioinf)"},{"key":"3394_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj-cs.341","volume":"7","author":"M Alshahrani","year":"2021","unstructured":"Alshahrani M, Thafar MA, Essack M. Application and evaluation of knowledge graph embeddings in biomedical data. PeerJ Comput Sci. 2021;7:1\u201328.","journal-title":"PeerJ Comput Sci"},{"key":"3394_CR31","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1016\/j.csbj.2020.05.017","volume":"18","author":"DN Nicholson","year":"2020","unstructured":"Nicholson DN, Greene CS. Constructing knowledge graphs and their biomedical applications. Comput Struct Biotechnol J. 2020;18:1414\u201328. https:\/\/doi.org\/10.1016\/j.csbj.2020.05.017.","journal-title":"Comput Struct Biotechnol J"},{"key":"3394_CR32","unstructured":"Nguyen DQ. A survey of embedding models of entities and relationships for knowledge graph completion. arXiv. 2017. http:\/\/arxiv.org\/abs\/1703.08098."},{"key":"3394_CR33","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L. Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng. 2017;29:2724\u201343. http:\/\/www.ieee.org\/publicationsstandards\/publications\/rights\/index. http:\/\/http:\/\/ieeexplore.ieee.org\/document\/8047276\/.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3394_CR34","doi-asserted-by":"publisher","first-page":"147","DOI":"10.3390\/info12040147","volume":"12","author":"SK Mohamed","year":"2021","unstructured":"Mohamed SK, Mu\u00f1oz E, Novacek V. On training knowledge graph embedding models. Information. 2021;12:147. https:\/\/www.mdpi.com\/2078-2489\/12\/4\/147.","journal-title":"Information"},{"key":"3394_CR35","first-page":"614","volume":"A597","author":"M Cambon","year":"2018","unstructured":"Cambon M, Cherouali K, Kushwaha A, Giudicelli V, Duroux P, Kossida S, et al. IMGT\/mAb-DB and IMGT\/2Dstructure- DB for IMGT standard definition of an antibody: from receptor to amino acid changes. Journ\u00e9es Ouvertes de Biologie Informatique et de Math\u00e9matiques (JOBIM). 2018;A597:614\u201317. https:\/\/imgt.org\/IMGTposters\/522_Cambon_JOBIM2018_MPL_270718.pdf.","journal-title":"Journ\u00e9es Ouvertes de Biologie Informatique et de Math\u00e9matiques (JOBIM)"},{"key":"3394_CR36","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1038\/nrd.2015.17","volume":"15","author":"N Ferrara","year":"2016","unstructured":"Ferrara N, Adamis AP. Ten Years of anti-vascular endothelial growth factor therapy. Nat Rev Drug Discov. 2016;15:385\u2013403. https:\/\/escholarship.org\/uc\/item\/8fc8m0vp.","journal-title":"Nat Rev Drug Discov"},{"key":"3394_CR37","unstructured":"Ali M, et al. PyKEEN 1.0: a python library for training and evaluating knowledge graph embeddings. J Mach Learn Res. 2021;22. http:\/\/arxiv.org\/abs\/2007.14175."},{"key":"3394_CR38","unstructured":"Ali M, et al. Bringing light into the dark: a large-scale evaluation of knowledge graph embedding models under a unified framework. https:\/\/github.com\/pykeen\/benchmarking."},{"key":"3394_CR39","doi-asserted-by":"crossref","unstructured":"Nguyen DQ, Ustalov D, et al. (eds). A survey of embedding models of entities and relationships for knowledge graph completion. In: Ustalov D, et al. editors. Proceedings of the graph-based methods for natural language processing (TextGraphs); 2020; Barcelona, Spain (Online). Barcelona: Association for Computational Linguistics; 2020. p. 1\u201314. https:\/\/aclanthology.org\/2020.textgraphs-1.1\/.","DOI":"10.18653\/v1\/2020.textgraphs-1.1"},{"key":"3394_CR40","first-page":"31","volume":"2377","author":"GA Gesese","year":"2019","unstructured":"Gesese GA, Biswas R, Sack H. A comprehensive survey of knowledge graph embeddings with literals: techniques and applications. CEUR Workshop Proc. 2019;2377:31\u201340.","journal-title":"CEUR Workshop Proc"},{"key":"3394_CR41","doi-asserted-by":"crossref","unstructured":"Asefa Gesese G, et al. A survey on knowledge graph embeddings with literals: which model links better literally? 2021. http:\/\/rdf.freebase.com\/.","DOI":"10.3233\/SW-200404"},{"key":"3394_CR42","doi-asserted-by":"crossref","unstructured":"Wang M, Qiu L, Wang X. A survey on knowledge graph embeddings for link prediction. Symmetry. 2021;13.","DOI":"10.3390\/sym13030485"},{"key":"3394_CR43","unstructured":"Abboud R, Ceylan II, Lukasiewicz T, Salvatori T. BoxE: a box embedding model for knowledge base completion. Adv Neural Inf Process Syst 2020-Decem, 2020;1\u201313."},{"key":"3394_CR44","first-page":"2579","volume":"9","author":"L Van Der Maaten","year":"2008","unstructured":"Van Der Maaten L, Hinton G. Visualizing Data using t-SNE. J Mach Learn Res. 2008;9:2579\u2013605.","journal-title":"J Mach Learn Res"},{"key":"3394_CR45","doi-asserted-by":"crossref","unstructured":"Rajabi E, Kafaie S. Knowledge graphs and explainable AI in healthcare. 2022.","DOI":"10.18293\/SEKE2022-020"},{"key":"3394_CR46","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","volume":"19","author":"A Ma\u0107kiewicz","year":"1993","unstructured":"Ma\u0107kiewicz A, Ratajczak W. Principal components analysis (PCA). Comput Geosciences. 1993;19:303\u201342.","journal-title":"Comput Geosciences"},{"key":"3394_CR47","unstructured":"Amid E, Warmuth MK. TriMap: large-scale dimensionality reduction using triplets. 2019. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.datasets.make_s_curve.html. http:\/\/arxiv.org\/abs\/1910.00204."},{"key":"3394_CR48","doi-asserted-by":"publisher","first-page":"9","DOI":"10.20892\/j.issn.2095-3941.2016.0084","volume":"14","author":"BM Reid","year":"2017","unstructured":"Reid BM, Permuth JB, Sellers TA. Epidemiology of ovarian cancer: a review. Cancer Biol Med. 2017;14:9\u201332. https:\/\/www.cancerbiomed.org\/content\/14\/1\/9.","journal-title":"Cancer Biol Med"},{"key":"3394_CR49","first-page":"1","volume":"2","author":"UA Matulonis","year":"2016","unstructured":"Matulonis UA, et al. Ovarian cancer. Nat Rev Disease Primers. 2016;2:1\u201322.","journal-title":"Nat Rev Disease Primers"},{"key":"3394_CR50","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1097\/GCO.0b013e3283324114","volume":"22","author":"S Mabuchi","year":"2010","unstructured":"Mabuchi S, Morishige K, Kimura T. Use of monoclonal antibodies in the treatment of ovarian cancer. Curr Opin Obstet Gynecology. 2010;22:3\u20138.","journal-title":"Curr Opin Obstet Gynecology"},{"key":"3394_CR51","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.1200\/JCO.21.00306","volume":"39","author":"KN Moore","year":"2021","unstructured":"Moore KN, et al. Atezolizumab, Bevacizumab, and chemotherapy for newly diagnosed stage III or IV ovarian cancer: placebo-controlled randomized phase III trial (IMagyn050\/GOG 3015\/ENGOT-OV39). J Clin Oncol: Off J The Am Soc Clin Oncol. 2021;39:1842\u201355. https:\/\/pubmed.ncbi.nlm.nih.gov\/33891472\/.","journal-title":"J Clin Oncol: Off J The Am Soc Clin Oncol"},{"key":"3394_CR52","doi-asserted-by":"crossref","unstructured":"Marm\u00e9 F, et al. Atezolizumab versus placebo in combination with bevacizumab and non-platinum-based chemotherapy in recurrent ovarian cancer: final overall and progression-free survival results from the AGO-OVAR 2.29\/ENGOT-ov34 study. J Clin Oncol. 2024;42:LBA5501\u20135501. https:\/\/ascopubs.org\/doi\/10.1200\/JCO.2024.42.17_suppl.LBA5501.","DOI":"10.1200\/JCO.2024.42.17_suppl.LBA5501"},{"key":"3394_CR53","unstructured":"Gonz\u00e1lez-Mart\u00edn A, et al. Atezolizumab combined with platinum and maintenance niraparib for recurrent ovarian cancer with a platinum-free Interval\u00bf6 months: ENGOT-OV41\/GEICO 69-O\/ANITA phase III trial. J Clin Oncol. 2024. https:\/\/ascopubs.org\/doi\/10.1200\/JCO.24.00668."},{"key":"3394_CR54","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1056\/NEJM199510193331606","volume":"333","author":"C Iril","year":"1995","unstructured":"Iril C, Ozman R, Milio E, Ontserrat M. Chronic lymphocytic leukemia. N Engl J Med. 1995;333:1052\u201357. https:\/\/www.nejm.org\/doi\/full\/10.1056\/NEJM199510193331606.","journal-title":"N Engl J Med"},{"key":"3394_CR55","doi-asserted-by":"publisher","first-page":"3705","DOI":"10.1182\/blood-2010-04-001230","volume":"116","author":"SM Jaglowski","year":"2010","unstructured":"Jaglowski SM, Alinari L, Lapalombella R, Muthusamy N, Byrd JC. The clinical application of monoclonal antibodies in chronic lymphocytic leukemia. Blood. 2010;116:3705\u201314. https:\/\/doi.org\/10.1182\/blood-2010-04-001230.","journal-title":"Blood"},{"key":"3394_CR56","doi-asserted-by":"crossref","unstructured":"Shirley M. Glofitamab: first approval. Drugs. 2023;83(1). https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10245362\/.","DOI":"10.1007\/s40265-023-01894-5"},{"key":"3394_CR57","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1200\/JCO.20.03175","volume":"39","author":"M Hutchings","year":"2021","unstructured":"Hutchings M, et al. Glofitamab, a novel, bivalent CD20-targeting T-Cell- engaging bispecific antibody, induces durable complete remissions in relapsed or refractory B-Cell lymphoma: a phase I trial. J Clin Oncol: Off J The Am Soc Clin Oncol. 2021;39:1959\u201370. https:\/\/pubmed.ncbi.nlm.nih.gov\/33739857\/.","journal-title":"J Clin Oncol: Off J The Am Soc Clin Oncol"},{"key":"3394_CR58","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1002\/hon.3163_28","volume":"41","author":"C Carlo-Stella","year":"2023","unstructured":"Carlo-Stella C, et al. Glofitamab monotherapy induces durable complete remissions and has a manageable safety profile in patients with Richter\u2019S transformation. Hematological Oncol. 2023;41:63\u201365. https:\/\/cllsociety.org\/2023\/11\/glofitamab-for-richters-transformation-gives-durable-remission\/.","journal-title":"Hematological Oncol"},{"key":"3394_CR59","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1182\/blood-2022-167285","volume":"140","author":"E Calabretta","year":"2022","unstructured":"Calabretta E, Hamadani M, Zinzani PL, Caimi P, Carlo-Stella C. The antibody-drug conjugate loncastuximab tesirine for the treatment of diffuse large B-cell lymphoma. Blood. 2022;140:303. https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9335500\/.","journal-title":"Blood"},{"key":"3394_CR60","doi-asserted-by":"publisher","unstructured":"Perdomo-Quinteiro P, Belmonte-Hern\u00e1ndez A. Knowledge graphs for drug repurposing: a review of databases and methods. 2024. https:\/\/doi.org\/10.1093\/bib\/bbae461.","DOI":"10.1093\/bib\/bbae461"},{"key":"3394_CR61","doi-asserted-by":"crossref","unstructured":"Lou P, et al. Potential target discovery and drug repurposing for coronaviruses: study involving a knowledge graph\u2013based approach. J Med Internet Res. 2023;25. https:\/\/www.jmir.org\/2023\/1\/e45225.","DOI":"10.2196\/45225"},{"key":"3394_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41392-021-00868-x","volume":"7","author":"S Jin","year":"2022","unstructured":"Jin S, et al. Emerging new therapeutic antibody derivatives for cancer treatment. Signal Transduct Targeted Ther. 2022;7:1\u201310.","journal-title":"Signal Transduct Targeted Ther"},{"key":"3394_CR63","unstructured":"Galkin M, Yuan X, Mostafa H, Tang J, Zhu Z. Towards foundation models for knowledge graph reasoning. ArXiv. 2023;abs\/2310.04562. https:\/\/api.semanticscholar.org\/CorpusID:263831485."},{"key":"3394_CR64","doi-asserted-by":"crossref","unstructured":"Cui Y, Sun Z, Hu W. A prompt-based knowledge graph foundation model for universal in-context reasoning. arXiv preprint arXiv:2410.12288 (2024). https:\/\/arxiv.org\/abs\/2410.12288 .","DOI":"10.52202\/079017-0227"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03394-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03394-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03394-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:34:09Z","timestamp":1774614849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03394-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,20]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3394"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03394-4","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,20]]},"assertion":[{"value":"8 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"89"}}