{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T22:36:47Z","timestamp":1771886207079,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031093418","type":"print"},{"value":"9783031093425","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-09342-5_3","type":"book-chapter","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:05:42Z","timestamp":1657285542000},"page":"24-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Knowledge Graph Completion Method Applied to Literature-Based Discovery for Predicting Missing Links Targeting Cancer Drug Repurposing"],"prefix":"10.1007","author":[{"given":"Ali","family":"Daowd","sequence":"first","affiliation":[]},{"given":"Samina","family":"Abidi","sequence":"additional","affiliation":[]},{"given":"Syed Sibte Raza","family":"Abidi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/nrd.2018.168","volume":"18","author":"S Pushpakom","year":"2019","unstructured":"Pushpakom, S., et al.: Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Disc. 18, 41\u201358 (2019)","journal-title":"Nat. Rev. Drug Disc."},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"103696","DOI":"10.1016\/j.jbi.2021.103696","volume":"115","author":"R Zhang","year":"2021","unstructured":"Zhang, R., Hristovski, D., Schutte, D., Kastrin, A., Fiszman, M., Kilicoglu, H.: Drug repurposing for COVID-19 via knowledge graph completion. J. Biomed. Inform. 115, 103696 (2021)","journal-title":"J. Biomed. Inform."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/fi13010014","volume":"13","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Che, C.: Drug repurposing for Parkinson\u2019s disease by integrating knowledge graph completion model and knowledge fusion of medical literature. Future Internet 13, 14 (2021)","journal-title":"Future Internet"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-3517-7","volume":"21","author":"H Kilicoglu","year":"2020","unstructured":"Kilicoglu, H., Rosemblat, G., Fiszman, M., Shin, D.: Broad-coverage biomedical relation extraction with SemRep. BMC Bioinform. 21, 1\u201328 (2020). https:\/\/doi.org\/10.1186\/s12859-020-3517-7","journal-title":"BMC Bioinform."},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"192435","DOI":"10.1109\/ACCESS.2020.3030076","volume":"8","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Wang, Y., Zhao, B., Cheng, J., Zhao, X., Duan, Z.: Knowledge graph completion: a review. IEEE Access 8, 192435\u2013192456 (2020)","journal-title":"IEEE Access"},{"key":"3_CR6","first-page":"1","volume":"15","author":"A Rossi","year":"2021","unstructured":"Rossi, A., Barbosa, D., Firmani, D., Matinata, A., Merialdo, P.: Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans. Knowl. Disc. Data (TKDD) 15, 1\u201349 (2021)","journal-title":"ACM Trans. Knowl. Disc. Data (TKDD)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Pai, S., Costabello, L.: Learning embeddings from knowledge graphs with numeric edge attributes. arXiv preprint https:\/\/arxiv.org\/abs\/2105.08683 (2021)","DOI":"10.24963\/ijcai.2021\/395"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"e0258626","DOI":"10.1371\/journal.pone.0258626","volume":"16","author":"W Choi","year":"2021","unstructured":"Choi, W., Lee, H.: Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions. PLoS ONE 16, e0258626 (2021)","journal-title":"PLoS ONE"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-59137-3_12","volume-title":"Artificial Intelligence in Medicine","author":"K Bougiatiotis","year":"2020","unstructured":"Bougiatiotis, K., Aisopos, F., Nentidis, A., Krithara, A., Paliouras, G.: Drug-drug interaction prediction on a biomedical literature knowledge graph. In: Michalowski, M., Moskovitch, R. (eds.) Artificial Intelligence in Medicine, vol. 12299, pp. 122\u2013132. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59137-3_12"},{"key":"3_CR10","first-page":"449","volume":"2020","author":"V Nov\u00e1\u010dek","year":"2020","unstructured":"Nov\u00e1\u010dek, V., Mohamed, S.K.: Predicting polypharmacy side-effects using knowledge graph embeddings. AMIA Summits Transl. Sci. Proc. 2020, 449 (2020)","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Chang, D., Bala\u017eevi\u0107, I., Allen, C., Chawla, D., Brandt, C., Taylor, R.A.: Benchmark and best practices for biomedical knowledge graph embeddings. In: Proceedings of the Conference. Association for Computational Linguistics. Meeting, vol. 2020, p. 167 (2020)","DOI":"10.18653\/v1\/2020.bionlp-1.18"},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.jbi.2017.08.011","volume":"74","author":"S Henry","year":"2017","unstructured":"Henry, S., McInnes, B.T.: Literature based discovery: models, methods, and trends. J. Biomed. Inform. 74, 20\u201332 (2017)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"3_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s10549-016-3774-3","volume":"157","author":"CP Capper","year":"2016","unstructured":"Capper, C.P., Larios, J.M., Sikora, M.J., Johnson, M.D., Rae, J.M.: The CYP17A1 inhibitor abiraterone exhibits estrogen receptor agonist activity in breast cancer. Breast Cancer Res. Treat. 157(1), 23\u201330 (2016). https:\/\/doi.org\/10.1007\/s10549-016-3774-3","journal-title":"Breast Cancer Res. Treat."},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.1021\/jm8013822","volume":"52","author":"M K\u00fchnle","year":"2009","unstructured":"K\u00fchnle, M., et al.: Potent and selective inhibitors of breast cancer resistance protein (ABCG2) derived from the p-glycoprotein (ABCB1) modulator tariquidar. J. Med. Chem. 52, 1190\u20131197 (2009)","journal-title":"J. Med. Chem."},{"key":"3_CR15","first-page":"6655","volume":"35","author":"K Schmidt","year":"2015","unstructured":"Schmidt, K., et al.: Targeting fibroblast growth factor receptor (FGFR) with BGJ398 in a gastric cancer model. Anticancer Res. 35, 6655\u20136665 (2015)","journal-title":"Anticancer Res."},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.ejpb.2018.10.018","volume":"133","author":"M Bar-Zeev","year":"2018","unstructured":"Bar-Zeev, M., Kelmansky, D., Assaraf, Y.G., Livney, Y.D.: \u0392-Casein micelles for oral delivery of SN-38 and elacridar to overcome BCRP-mediated multidrug resistance in gastric cancer. Eur. J. Pharm. Biopharm. 133, 240\u2013249 (2018)","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"3_CR17","unstructured":"Costabello, L., Pai, S., Van, C.L., McGrath, R., McCarthy, N., Tabacof, P.: AmpliGraph: a library for representation learning on knowledge graphs (2019)"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Daowd, A., Barrett, M., Abidi, S., Abidi, S.S.R.: A framework to build a causal knowledge graph for chronic diseases and cancers by discovering semantic associations from biomedical literature. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 13\u201322. IEEE (2021)","DOI":"10.1109\/ICHI52183.2021.00016"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09342-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:05:58Z","timestamp":1657285558000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09342-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031093418","9783031093425"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09342-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"9 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Halifax, NS","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime22.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"113","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}