{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T08:44:59Z","timestamp":1768293899237,"version":"3.49.0"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031958373","type":"print"},{"value":"9783031958380","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-95838-0_17","type":"book-chapter","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:14:35Z","timestamp":1750590875000},"page":"170-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph Representation Learning for\u00a0IBD Diagnosis Based on\u00a0Microbiome Metaomic Data"],"prefix":"10.1007","author":[{"given":"Christopher","family":"Irwin","sequence":"first","affiliation":[]},{"given":"Flavio","family":"Mignone","sequence":"additional","affiliation":[]},{"given":"Stefania","family":"Montani","sequence":"additional","affiliation":[]},{"given":"Luigi","family":"Portinale","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"17_CR1","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 and Data Mining (2019)","DOI":"10.1145\/3292500.3330701"},{"key":"17_CR2","unstructured":"Dwivedi, V.P., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Graph neural networks with learnable structural and positional representations. arXiv preprint arXiv:2110.07875 (2021)"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Grover, A., Leskovec, J.: NODE2VEC: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864. KDD 2016, Association for Computing Machinery, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2939672.2939754, https:\/\/doi.org\/10.1145\/2939672.2939754","DOI":"10.1145\/2939672.2939754"},{"key":"17_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01588-5","volume-title":"Graph Representation Learning","author":"W Hamilton","year":"2020","unstructured":"Hamilton, W.: Graph Representation Learning. Springer, Synthesis Lectures on Artificial Intelligence and Machine Learning (2020). https:\/\/doi.org\/10.1007\/978-3-031-01588-5"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Hern\u00e1ndez\u00a0Medina, R., Kutuzova, S., Nielsen, K.N., Johansen, J., Hansen, L.H., Nielsen, M., Rasmussen, S.: Machine learning and deep learning applications in microbiome research. ISME Communications 2(1), 98 (2022). https:\/\/doi.org\/10.1038\/s43705-022-00182-9, https:\/\/doi.org\/10.1038\/s43705-022-00182-9","DOI":"10.1038\/s43705-022-00182-9"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Jiang, Y., Aton, M., Zhu, Q., Lu, Y.Y.: MIOSTONE: modeling microbiome-trait associations with taxonomy-adaptive neural networks. bioRxiv (2024).https:\/\/doi.org\/10.1101\/2023.11.04.565596, https:\/\/www.biorxiv.org\/content\/early\/2024\/02\/04\/2023.11.04.565596","DOI":"10.1101\/2023.11.04.565596"},{"issue":"7758","key":"17_CR7","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1038\/s41586-019-1237-9","volume":"569","author":"J Lloyd-Price","year":"2019","unstructured":"Lloyd-Price, J., et al.: Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569(7758), 655\u2013662 (2019)","journal-title":"Nature"},{"issue":"10","key":"17_CR8","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1109\/JBHI.2020.2993761","volume":"24","author":"D Reiman","year":"2020","unstructured":"Reiman, D., Metwally, A.A., Sun, J., Dai, Y.: PopPhy-CNN: a phylogenetic tree embedded architecture for convolutional neural networks to predict host phenotype from metagenomic data. IEEE J. Biomed. Health Inform. 24(10), 2993\u20133001 (2020). https:\/\/doi.org\/10.1109\/JBHI.2020.2993761","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Sharma, D., Paterson, A.D., Xu, W.: TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction. Bioinformatics 36(17), 4544\u20134550 (2020). https:\/\/doi.org\/10.1093\/bioinformatics\/btaa542, https:\/\/doi.org\/10.1093\/bioinformatics\/btaa542","DOI":"10.1093\/bioinformatics\/btaa542"},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Wekesa, J.S., Kimwele, M.: A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Front. Genetics 14 (2023). https:\/\/doi.org\/10.3389\/fgene.2023.1199087, https:\/\/www.frontiersin.org\/journals\/genetics\/articles\/10.3389\/fgene.2023.1199087","DOI":"10.3389\/fgene.2023.1199087"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Zheng, J., Sun, Q., Zhang, J., Ng, S.: The role of gut microbiome in inflammatory bowel disease diagnosis and prognosis. United Eur. Gastroenterol J. 10(10), 1091\u20131102 (2022). https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9752296\/","DOI":"10.1002\/ueg2.12338"}],"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\/978-3-031-95838-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:14:36Z","timestamp":1750590876000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95838-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031958373","9783031958380"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95838-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 June 2025","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":"Pavia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime25.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}