{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:23:10Z","timestamp":1742991790667,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031534676"},{"type":"electronic","value":"9783031534683"}],"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\/978-3-031-53468-3_33","type":"book-chapter","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T13:03:57Z","timestamp":1708347837000},"page":"389-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DFI-DGCF: A Graph-Based Recommendation Approach for\u00a0Drug-Food Interactions"],"prefix":"10.1007","author":[{"given":"Sofia","family":"Bourhim","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"33_CR1","doi-asserted-by":"publisher","unstructured":"Wishart, D., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research. 46, D1074\u2013D1082 (2018). https:\/\/doi.org\/10.1093\/nar\/gkx1037","DOI":"10.1093\/nar\/gkx1037"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"D618","DOI":"10.1093\/nar\/gkn815","volume":"37","author":"D Wishart","year":"2009","unstructured":"Wishart, D., et al.: FoodDB: a comprehensive food database for dietary studies, research, and education. Nucleic Acids Res. 37, D618\u2013D623 (2009). https:\/\/doi.org\/10.1093\/nar\/gkn815","journal-title":"Nucleic Acids Res."},{"key":"33_CR3","doi-asserted-by":"publisher","first-page":"E4304","DOI":"10.1073\/pnas.1803294115","volume":"115","author":"J Ryu","year":"2018","unstructured":"Ryu, J., Kim, H., Lee, S.: Deep learning improves prediction of drug-drug and drug-food interactions. Proc. Natl. Acad. Sci. 115, E4304\u2013E4311 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"131144","DOI":"10.1109\/ACCESS.2022.3230323","volume":"10","author":"S Bourhim","year":"2022","unstructured":"Bourhim, S., Benhiba, L., Idrissi, M.: A community-driven deep collaborative approach for recommender systems. IEEE Access. 10, 131144\u2013131152 (2022)","journal-title":"IEEE Access."},{"key":"33_CR5","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings Of The Twenty-Fifth Conference On Uncertainty In Artificial Intelligence (2012)"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings Of The 24th ACM International On Conference On Information And Knowledge Management, pp. 891\u2013900 (2015)","DOI":"10.1145\/2806416.2806512"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings Of The 24th International Conference On World Wide Web, pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings Of The 22nd ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, pp. 1225\u20131234 (2016)","DOI":"10.1145\/2939672.2939753"},{"key":"33_CR9","unstructured":"Kipf, T., Welling, M.: Variational graph auto-encoders. In: International Conference On Learning Representations (2017)"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings Of The 22nd ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, pp. 1105\u20131114 (2016)","DOI":"10.1145\/2939672.2939751"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Wang, T., et al.: DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions. Bioinformatics. 39, btac837 (2023)","DOI":"10.1093\/bioinformatics\/btac837"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Bourhim, S., Benhiba, L., Idrissi, M.: Towards a Novel Graph-based collaborative filtering approach for recommendation systems. In: Proceedings Of The 12th International Conference On Intelligent Systems: Theories And Applications, pp. 1\u20136 (2018)","DOI":"10.1145\/3289402.3289524"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Bourhim, S., Benhiba, L., Idrissi, M.: Investigating algorithmic variations of an RS Graph-based collaborative filtering approach. In: Proceedings Of The ArabWIC 6th Annual International Conference Research Track, pp. 1\u20136 (2019)","DOI":"10.1145\/3333165.3333177"}],"container-title":["Studies in Computational Intelligence","Complex Networks & Their Applications XII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53468-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T13:12:18Z","timestamp":1718629938000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53468-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031534676","9783031534683"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53468-3_33","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COMPLEX NETWORKS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Complex Networks and Their Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Menton","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwcna2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.complexnetworks.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}