{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T16:13:25Z","timestamp":1768148005542,"version":"3.49.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030862299","type":"print"},{"value":"9783030862305","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86230-5_46","type":"book-chapter","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:03:00Z","timestamp":1631005380000},"page":"584-595","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Biomedical Knowledge Graph Embeddings for Personalized Medicine"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2465-5795","authenticated-orcid":false,"given":"Joana","family":"Vilela","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9144-7630","authenticated-orcid":false,"given":"Muhammad","family":"Asif","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4144-7020","authenticated-orcid":false,"given":"Ana Rita","family":"Marques","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2797-5513","authenticated-orcid":false,"given":"Jo\u00e3o Xavier","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9969-6241","authenticated-orcid":false,"given":"C\u00e9lia","family":"Rasga","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7134-8037","authenticated-orcid":false,"given":"Astrid","family":"Vicente","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2490-8913","authenticated-orcid":false,"given":"Hugo","family":"Martiniano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"46_CR1","unstructured":"Diagnostic and Statistical Manual of Mental Disorders: Dsm-5. Amer Psychiatric Pub Incorporated (2013), google-Books-ID: EIbMlwEACAAJ"},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation Hyperparameter Optimization Framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 2623\u20132631. Association for Computing Machinery, New York (July 2019). https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"46_CR3","doi-asserted-by":"publisher","unstructured":"Asif, M., Martiniano, H.F.M.C.M., Vicente, A.M., Couto, F.M.: Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology. PLoS One 13(12), 1\u201315 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0208626","DOI":"10.1371\/journal.pone.0208626"},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Asif, M., et al.: Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning. bioRxiv p. 470757 (2019)","DOI":"10.1101\/470757"},{"key":"46_CR5","doi-asserted-by":"publisher","unstructured":"Aurilio, G., et al.: Androgen receptor signaling pathway in prostate cancer: from genetics to clinical applications. Cells 9(12) (2020). https:\/\/doi.org\/10.3390\/cells9122653","DOI":"10.3390\/cells9122653"},{"key":"46_CR6","doi-asserted-by":"publisher","unstructured":"Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium: Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol. Autism 8, 21 (2017). https:\/\/doi.org\/10.1186\/s13229-017-0137-9","DOI":"10.1186\/s13229-017-0137-9"},{"key":"46_CR7","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 2787\u20132795. Curran Associates, Inc. (2013). http:\/\/papers.nips.cc\/paper\/5071-translating-embeddings-for-modeling-multi-relational-data.pdf"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Boyle, E.A., Li, Y.I., Pritchard, J.K.: An expanded view of complex traits: from polygenic to omnigenic. Cell 169(7), 1177\u20131186 (2017)","DOI":"10.1016\/j.cell.2017.05.038"},{"key":"46_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/978-3-642-37456-2_14","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"RJGB Campello","year":"2013","unstructured":"Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160\u2013172. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-37456-2_14"},{"key":"46_CR10","doi-asserted-by":"publisher","unstructured":"Fleming, L., et al.: Genotype-phenotype correlation of congenital anomalies in multiple congenital anomalies hypotonia seizures syndrome (MCAHS1)\/PIGN-related epilepsy. Am. J. Med. Genet.. Part A 170A(1), 77\u201386 (2016). https:\/\/doi.org\/10.1002\/ajmg.a.37369","DOI":"10.1002\/ajmg.a.37369"},{"key":"46_CR11","doi-asserted-by":"publisher","unstructured":"Goetz, L.H., Schork, N.J.: Personalized medicine: motivation, challenges, and progress. Fertil. Steril. 109(6), 952\u2013963 (2018). https:\/\/doi.org\/10.1016\/j.fertnstert.2018.05.006","DOI":"10.1016\/j.fertnstert.2018.05.006"},{"key":"46_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-3-030-34585-3_21","volume-title":"Computational Intelligence Methods for Bioinformatics and Biostatistics","author":"HFMC Martiniano","year":"2020","unstructured":"Martiniano, H.F.M.C., Asif, M., Vicente, A.M., Correia, L.: Network propagation-based semi-supervised identification of genes associated with autism spectrum disorder. In: Raposo, M., Ribeiro, P., S\u00e9rio, S., Staiano, A., Ciaramella, A. (eds.) CIBB 2018. LNCS, vol. 11925, pp. 239\u2013248. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-34585-3_21"},{"key":"46_CR13","doi-asserted-by":"publisher","unstructured":"Maydan, G., et al.: Multiple congenital anomalies-hypotonia-seizures syndrome is caused by a mutation in PIGN. J. Med. Genet. 48(6), 383\u2013389 (2011). https:\/\/doi.org\/10.1136\/jmg.2010.087114","DOI":"10.1136\/jmg.2010.087114"},{"key":"46_CR14","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426 [cs, stat] (December 2018), http:\/\/arxiv.org\/abs\/1802.03426, arXiv: 1802.03426"},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"Mohamed, S.K., Nounu, A., Nov\u00e1\u010dek, V.: Biological applications of knowledge graph embedding models. Briefings Bioinform. 22(2), 1679\u20131693 (2021)","DOI":"10.1093\/bib\/bbaa012"},{"key":"46_CR16","doi-asserted-by":"publisher","unstructured":"Moulavi, D., Jaskowiak, P.A., Campello, R.J.G.B., Zimek, A., Sander, J.: Density-Based Clustering Validation. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 839\u2013847. Proceedings, Society for Industrial and Applied Mathematics (April 2014). https:\/\/doi.org\/10.1137\/1.9781611973440.96, https:\/\/epubs.siam.org\/doi\/10.1137\/1.9781611973440.96","DOI":"10.1137\/1.9781611973440.96"},{"key":"46_CR17","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1016\/j.csbj.2020.05.017","volume":"18","author":"DN Nicholson","year":"2020","unstructured":"Nicholson, D.N., Greene, C.S.: Constructing knowledge graphs and their biomedical applications. Comput. Struct. Biotech. J. 18, 1414\u20131428 (2020). https:\/\/doi.org\/10.1016\/j.csbj.2020.05.017","journal-title":"Comput. Struct. Biotech. J."},{"key":"46_CR18","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, E., Bouchard, G.: Complex embeddings for simple link prediction. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 2071\u20132080. PMLR, New York (June 2016). http:\/\/proceedings.mlr.press\/v48\/trouillon16.html"},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Vicente, A.M., Ballensiefen, W., J\u00f6nsson, J.I.: How personalised medicine will transform healthcare by 2030: the ICPerMed vision. J. Transl. Med. 18(1), 180 (2020)","DOI":"10.1186\/s12967-020-02316-w"},{"key":"46_CR20","doi-asserted-by":"publisher","unstructured":"Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724\u20132743 (2017). https:\/\/doi.org\/10.1109\/TKDE.2017.2754499","DOI":"10.1109\/TKDE.2017.2754499"},{"key":"46_CR21","unstructured":"Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding Entities and Relations for Learning and Inference in Knowledge Bases. arXiv:1412.6575 [cs] (August 2015), http:\/\/arxiv.org\/abs\/1412.6575, arXiv: 1412.6575"},{"key":"46_CR22","doi-asserted-by":"crossref","unstructured":"Zheng, D., et al.: DGL-KE: Training Knowledge Graph Embeddings at Scale. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, pp. 739\u2013748. Association for Computing Machinery, New York (2020)","DOI":"10.1145\/3397271.3401172"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86230-5_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:14:10Z","timestamp":1631006050000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86230-5_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030862299","9783030862305"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86230-5_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","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":"epia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.appia.pt\/epia2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"108","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":"62","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":"0","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":"57% - 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":"3.47","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":"1.36","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)"}}]}}