{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:48:59Z","timestamp":1743007739485,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819947485"},{"type":"electronic","value":"9789819947492"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-4749-2_59","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T23:02:17Z","timestamp":1690671737000},"page":"687-699","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["NIEE: Modeling Edge Embeddings for Drug-Disease Association Prediction via Neighborhood Interactions"],"prefix":"10.1007","author":[{"given":"Yu","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Jingli","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yulin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Junyi","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"issue":"8","key":"59_CR1","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/nrd1468","volume":"3","author":"TT Ashburn","year":"2004","unstructured":"Ashburn, T.T., Thor, K.B.: Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3(8), 673\u2013683 (2004)","journal-title":"Nat. Rev. Drug Discov."},{"issue":"2","key":"59_CR2","doi-asserted-by":"publisher","first-page":"bbab581","DOI":"10.1093\/bib\/bbab581","volume":"23","author":"Y Meng","year":"2022","unstructured":"Meng, Y., Changcheng, L., Jin, M., Junlin, X., Zeng, X., Yang, J.: A weighted bilinear neural collaborative filtering approach for drug repositioning. Briefings Bioinform. 23(2), bbab581 (2022)","journal-title":"Briefings Bioinform."},{"key":"59_CR3","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1038\/s41467-018-05116-5","volume":"9","author":"F Cheng","year":"2018","unstructured":"Cheng, F., Desai, R.J., Handy, D.E., et al.: Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun 9, 2691 (2018)","journal-title":"Nat Commun"},{"issue":"2","key":"59_CR4","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1093\/bioinformatics\/btab651","volume":"38","author":"H Fu","year":"2022","unstructured":"Fu, H., Huang, F., Liu, X., et al.: MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks. Bioinformatics 38(2), 426\u2013434 (2022)","journal-title":"Bioinformatics"},{"key":"59_CR5","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1145\/2623330.2623732","volume-title":"The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 24\u201327","author":"B Perozzi","year":"2014","unstructured":"Perozzi, B., et al.: DeepWalk: online learning of social representations. In: Macskassy, S.A., et al. (eds.) The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 24\u201327, pp. 701\u2013710. ACM, New York, NY, USA (2014)"},{"doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., et al.: LINE: large-scale Information Network Embedding. In: Proceedings of the 24th international conference on world wide web, pp. 1067\u20131077. International World Wide Web Conferences Steering Committee, Florence (2015)","key":"59_CR6","DOI":"10.1145\/2736277.2741093"},{"doi-asserted-by":"crossref","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. Association for Computing Machinery, New York (2016)","key":"59_CR7","DOI":"10.1145\/2939672.2939754"},{"doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., et al.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), pp. 1225\u20131234. Association for Computing Machinery, New York (2016)","key":"59_CR8","DOI":"10.1145\/2939672.2939753"},{"doi-asserted-by":"publisher","unstructured":"N. Kipf, T.,Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017). https:\/\/doi.org\/10.48550\/arXiv.1609.02907","key":"59_CR9","DOI":"10.48550\/arXiv.1609.02907"},{"unstructured":"Velickovic, P., et al.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 3\u2013May 3 2018, Conference Track Proceedings. OpenReview.net (2018)","key":"59_CR10"},{"doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135\u2013144. ACM, Halifax (2017)","key":"59_CR11","DOI":"10.1145\/3097983.3098036"},{"doi-asserted-by":"crossref","unstructured":"Fu, T.-Y., Lee, W.-C., Lei, Z.: HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp. 1797\u2013806. Singapore (2017)","key":"59_CR12","DOI":"10.1145\/3132847.3132953"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Heterogeneous graph attention network. WWW 2019, The Web Conference, pp. 2022\u20132032. ACM, San Francisco (2019)","key":"59_CR13","DOI":"10.1145\/3308558.3313562"},{"doi-asserted-by":"publisher","unstructured":"Fu, X., et al.: MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. WWW 2020: The Web Conference 2020, pp. 2331\u20132341. ACM\/IW3C2, Taipei (2020). https:\/\/doi.org\/10.1145\/3366423.3380297","key":"59_CR14","DOI":"10.1145\/3366423.3380297"},{"doi-asserted-by":"crossref","unstructured":"Jin, J., Qin, J., et al.: An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 75\u201384. Virtual Event (2020)","key":"59_CR15","DOI":"10.1145\/3394486.3403050"},{"unstructured":"Malacards Homepage. https:\/\/www.malacards.org\/. Accessed 27 Mar 2023","key":"59_CR16"},{"issue":"10","key":"59_CR17","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.1109\/TKDE.2013.2297920","volume":"26","author":"C Shi","year":"2014","unstructured":"Shi, C., Kong, X., Huang, Y., Yu, P.S., Bin, W.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479\u20132492 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"59_CR18","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1186\/s12859-021-04099-3","volume":"22","author":"M He","year":"2021","unstructured":"He, M., et al.: Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction. BMC Bioinf. 22(1), 165 (2021)","journal-title":"BMC Bioinf."},{"issue":"11","key":"59_CR19","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1001\/jamaneurol.2021.2942","volume":"78","author":"C Fredericks","year":"2021","unstructured":"Fredericks, C.: Methylphenidate for apathy in Alzheimer disease\u2014why should we care? JAMA Neurol. 78(11), 1311 (2021)","journal-title":"JAMA Neurol."},{"issue":"5","key":"59_CR20","doi-asserted-by":"publisher","first-page":"831","DOI":"10.2147\/ndt.s3685","volume":"4","author":"JM Alisky","year":"2008","unstructured":"Alisky, J.M.: Intrathecal corticosteroids might slow Alzheimer\u2019s disease progression. Neuropsychiatr Dis Treat 4(5), 831\u2013833 (2008). https:\/\/doi.org\/10.2147\/ndt.s3685. PMID: 19183775; PMCID: PMC2626920","journal-title":"Neuropsychiatr Dis Treat"},{"unstructured":"The Comparative Toxicogenomics Database | CTD Homepage. http:\/\/ctdbase.org\/. Accessed 31 Mar 2023","key":"59_CR21"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4749-2_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T06:09:06Z","timestamp":1693548546000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4749-2_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947485","9789819947492"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4749-2_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}