{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:20:14Z","timestamp":1742916014032,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031703775"},{"type":"electronic","value":"9783031703782"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70378-2_18","type":"book-chapter","created":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:02:05Z","timestamp":1725181325000},"page":"287-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SAGS-DynamicBio: Integrating Semantic-Aware and\u00a0Graph Structure-Aware Embedding for\u00a0Dynamic Biological Data with\u00a0Knowledge Graphs"],"prefix":"10.1007","author":[{"given":"Yao","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yongfei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Alvarez-Mamani, E., et al.: Graph embedding on mass spectrometry-and sequencing-based biomedical data. BMC Bioinform. 25(1), 1 (2024)","DOI":"10.1186\/s12859-023-05612-6"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Veleiro, U., et al.: GENNIUS: an ultrafast drug-target interaction inference method based on graph neural networks. Bioinformatics 40(1), p.btad774 (2024)","DOI":"10.1093\/bioinformatics\/btad774"},{"key":"18_CR3","doi-asserted-by":"publisher","unstructured":"Ezzat, A., Zhao, P., Wu, M., Li, X.-L., Kwoh, C.-K.: Drug-target interaction prediction with graph regularized matrix factorization. In: IEEE\/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 3, pp. 646\u2013656, 1 May-June 2017. https:\/\/doi.org\/10.1109\/TCBB.2016.2530062.","DOI":"10.1109\/TCBB.2016.2530062."},{"issue":"1","key":"18_CR4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s12859-024-05684-y","volume":"25","author":"D Iliadis","year":"2024","unstructured":"Iliadis, D., De Baets, B., Pahikkala, T., Waegeman, W.: A comparison of embedding aggregation strategies in drug-target interaction prediction. BMC Bioinformatics 25(1), 59 (2024)","journal-title":"BMC Bioinformatics"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Chen, M., et al.: Drug-target interactions prediction based on signed heterogeneous graph neural networks. Chinese J. Electron. 33(1), 231\u2013244 (2024)","DOI":"10.23919\/cje.2022.00.384"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Li, N., et al.: Drug-target interaction prediction using knowledge graph embedding. iScience (2024)","DOI":"10.1016\/j.isci.2024.109393"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Ge, X., Wang, Y.C., Wang, B., Kuo, C.C.: Knowledge graph embedding: an overview. APSIPA Trans. Signal Inform. Process. 13(1) (2024)","DOI":"10.1561\/116.00000065"},{"issue":"10","key":"18_CR8","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1808","volume":"22","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Wang, P., Yang, D., Qiu, N.: A knowledge graph embedding model based attention mechanism for enhanced node information integration. PeerJ Computer Science. 22(10), e1808 (2024)","journal-title":"PeerJ Computer Science."},{"issue":"1","key":"18_CR9","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1093\/jamia\/ocad186","volume":"31","author":"J Sanjak","year":"2024","unstructured":"Sanjak, J., Binder, J., Yadaw, A.S., Zhu, Q., Math\u00e9, E.A.: Clustering rare diseases within an ontology-enriched knowledge graph. J. Am. Med. Inform. Assoc. 31(1), 154\u201364 (2024)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"18_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123542","volume":"20","author":"J Wang","year":"2024","unstructured":"Wang, J., Huang, H., Wu, Y., Zhang, F., Zhang, S., Guo, K.: Open knowledge graph link prediction with semantic-aware embedding. Expert Syst. Appl. 20, 123542 (2024)","journal-title":"Expert Syst. Appl."},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Guo, S., Wang, Q., Wang, B., et al.: Semantically smooth knowledge graph embedding. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 84\u201394 (2015)","DOI":"10.3115\/v1\/P15-1009"},{"issue":"2","key":"18_CR12","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1093\/bioinformatics\/btz600","volume":"36","author":"SK Mohamed","year":"2020","unstructured":"Mohamed, S.K., Nov\u00e1\u010dek, V., Nounu, A.: Discovering protein drug targets using knowledge graph embeddings. Bioinformatics 36(2), 603\u2013610 (2020)","journal-title":"Bioinformatics"},{"issue":"1","key":"18_CR13","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","volume":"35","author":"F Wan","year":"2019","unstructured":"Wan, F., Hong, L., Xiao, A., et al.: NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions. Bioinformatics 35(1), 104\u2013111 (2019)","journal-title":"Bioinformatics"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Peng, J., Wang, Y., Guan, J., et al.: An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction. Briefings Bioinfor. 22(5), bbaa430 (2021)","DOI":"10.1093\/bib\/bbaa430"},{"key":"18_CR15","unstructured":"Sun, Z., Deng, Z.H., Nie, J.Y., et al.: Rotate: knowledge graph embedding by relational rotation in complex space[J]. ar**v preprint ar**v:1902.10197 (2019)"},{"key":"18_CR16","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., et al.: Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26 (2013)"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., et al.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28(1) (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., et al.: Knowledge graph embedding via dynamic map** matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"key":"18_CR19","unstructured":"Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. ICML 11(10.5555), 3104482\u20133104584 (2011)"},{"key":"18_CR20","unstructured":"Yang, B., Yih, W., He, X., et al.: Embedding entities and relations for learning and inference in knowledge bases. ar**v preprint ar**v:1412.6575 (2014)"},{"key":"18_CR21","unstructured":"Trouillon, T., Welbl, J., Riedel, S., et al.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning. PMLR, pp. 2071\u20132080 (2016)"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cai, J., Zhang, Y., et al.: Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34(03), pp. 3065\u20133072 (2020)","DOI":"10.1609\/aaai.v34i03.5701"},{"key":"18_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00447-2","volume":"12","author":"MA Thafar","year":"2020","unstructured":"Thafar, M.A., Olayan, R.S., Ashoor, H., et al.: DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques[J]. J. Cheminformatics 12, 1\u201317 (2020)","journal-title":"J. Cheminformatics"},{"issue":"8","key":"18_CR24","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","volume":"37","author":"T Nguyen","year":"2021","unstructured":"Nguyen, T., Le, H., Quinn, T.P., et al.: GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37(8), 1140\u20131147 (2021)","journal-title":"Bioinformatics"},{"issue":"2","key":"18_CR25","doi-asserted-by":"publisher","first-page":"2141","DOI":"10.1093\/bib\/bbaa044","volume":"22","author":"T Zhao","year":"2021","unstructured":"Zhao, T., Hu, Y., Valsdottir, L.R., et al.: Identifying drug-target interactions based on graph convolutional network and deep neural network. Brief. Bioinform. 22(2), 2141\u20132150 (2021)","journal-title":"Brief. Bioinform."},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Kanehisa, M., Goto, S., Hattori, M., et al.: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34(suppl_1), D354-D357 (2006)","DOI":"10.1093\/nar\/gkj102"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Schomburg, I., Chang, A., Ebeling, C., et al.: BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 32(suppl_1), D431-D433 (2004)","DOI":"10.1093\/nar\/gkh081"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"G\u00fcnther, S., Kuhn, M., Dunkel, M., et al.: SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 36(suppl_1), D919\u2013D922 (2007)","DOI":"10.1093\/nar\/gkm862"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Wishart, D.S., Knox, C., Guo, A.C., et al.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36(suppl_1), D901\u2013D906 (2008)","DOI":"10.1093\/nar\/gkm958"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheng, M.: Drug-target interaction prediction based on knowledge graph embedding and BiLSTM networks. In: International Conference on Intelligent Computing. Singapore: Springer Nature Singapore, pp. 803\u2013813 (2023)","DOI":"10.1007\/978-981-99-4749-2_68"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"He, Z.: Drug-target interaction prediction based on knowledge graph and convolutional neural network integrated with CBAM module. In: International Conference on Intelligent Computing. Singapore: Springer Nature Singapore, pp. 653\u2013665 (2023)","DOI":"10.1007\/978-981-99-4749-2_56"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70378-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:05:49Z","timestamp":1725181549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70378-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703775","9783031703782"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70378-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}