{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:32:46Z","timestamp":1743042766326,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030914141"},{"type":"electronic","value":"9783030914158"}],"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\/978-3-030-91415-8_34","type":"book-chapter","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T18:04:03Z","timestamp":1637172243000},"page":"400-410","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Deep Learning Approach Based on Feature Reconstruction and Multi-dimensional Attention Mechanism for Drug-Drug Interaction Prediction"],"prefix":"10.1007","author":[{"given":"Jiang","family":"Xie","sequence":"first","affiliation":[]},{"given":"Jiaming","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Hongjian","family":"He","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Dong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"34_CR1","doi-asserted-by":"crossref","unstructured":"Chu, Y., et al.: DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Briefings Bioinf. 22(3), bbaa205 (2021)","DOI":"10.1093\/bib\/bbaa205"},{"issue":"15","key":"34_CR2","doi-asserted-by":"publisher","first-page":"4316","DOI":"10.1093\/bioinformatics\/btaa501","volume":"36","author":"Y Deng","year":"2020","unstructured":"Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W., Liu, S.: A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics 36(15), 4316\u20134322 (2020)","journal-title":"Bioinformatics"},{"issue":"9237","key":"34_CR3","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1016\/S0140-6736(00)02799-9","volume":"356","author":"IR Edwards","year":"2000","unstructured":"Edwards, I.R., Aronson, J.K.: Adverse drug reactions: definitions, diagnosis, and management. Lancet 356(9237), 1255\u20131259 (2000)","journal-title":"Lancet"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Foucquier, J., Guedj, M.: Analysis of drug combinations: current methodological landscape. Pharmacol. Res. Perspect. 3(3), e00149 (2015)","DOI":"10.1002\/prp2.149"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Kastrin, A., Ferk, P., Lesko\u0161ek, B.: Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning. PLOS ONE 13(5), e0196865 (2018)","DOI":"10.1371\/journal.pone.0196865"},{"key":"34_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Law, V., et al.: DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42(D1), D1091\u2013D1097 (2014)","DOI":"10.1093\/nar\/gkt1068"},{"issue":"24","key":"34_CR9","doi-asserted-by":"publisher","first-page":"5600","DOI":"10.1093\/bioinformatics\/btaa1074","volume":"36","author":"Z Lv","year":"2020","unstructured":"Lv, Z., Wang, P., Zou, Q., Jiang, Q.: Identification of sub-Golgi protein localization by use of deep representation learning features. Bioinformatics 36(24), 5600\u20135609 (2020)","journal-title":"Bioinformatics"},{"key":"34_CR10","first-page":"410","volume":"2012","author":"B Percha","year":"2012","unstructured":"Percha, B., Garten, Y., Altman, R.B.: Discovery and explanation of drug-drug interactions via text mining. Pac. Symp. Biocomput. 2012, 410\u2013421 (2012)","journal-title":"Pac. Symp. Biocomput."},{"key":"34_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-642-35289-8_5","volume-title":"Neural Networks: Tricks of the Trade","author":"L Prechelt","year":"2012","unstructured":"Prechelt, L.: Early stopping\u2014but when? In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 53\u201367. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_5"},{"issue":"18","key":"34_CR12","doi-asserted-by":"publisher","first-page":"E4304","DOI":"10.1073\/pnas.1803294115","volume":"115","author":"JY Ryu","year":"2018","unstructured":"Ryu, J.Y., Kim, H.U., Lee, S.Y.: Deep learning improves prediction of drug-drug and drug-food interactions. Proc. Nat. Acad. Sci. 115(18), E4304\u2013E4311 (2018)","journal-title":"Proc. Nat. Acad. Sci."},{"issue":"1","key":"34_CR13","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"34_CR14","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1021\/ci025584y","volume":"43","author":"C Steinbeck","year":"2003","unstructured":"Steinbeck, C., Han, Y., Kuhn, S., Horlacher, O., Luttmann, E., Willighagen, E.: The chemistry development kit (CDK): an open-source java library for chemo-and bioinformatics. J. Chem. Inf. Comput. Sci. 43(2), 493\u2013500 (2003)","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"6","key":"34_CR15","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1136\/amiajnl-2012-000935","volume":"19","author":"S Vilar","year":"2012","unstructured":"Vilar, S., Harpaz, R., Uriarte, E., Santana, L., Rabadan, R., Friedman, C.: Drug-drug interaction through molecular structure similarity analysis. J. Am. Med. Inform. Assoc. 19(6), 1066\u20131074 (2012)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"4","key":"34_CR16","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/72.80269","volume":"1","author":"EA Wan","year":"1990","unstructured":"Wan, E.A.: Neural network classification: a Bayesian interpretation. IEEE Trans. Neural Netw. 1(4), 303\u2013305 (1990)","journal-title":"IEEE Trans. Neural Netw."},{"key":"34_CR17","unstructured":"Xu, H., Sang, S., Lu, H.: Tri-graph information propagation for polypharmacy side effect prediction. arXiv preprint arXiv:2001.10516 (2020)"},{"issue":"4","key":"34_CR18","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1093\/bioinformatics\/btz699","volume":"36","author":"M Zeng","year":"2020","unstructured":"Zeng, M., Zhang, F., Wu, F.X., Li, Y., Wang, J., Li, M.: Protein-protein interaction site prediction through combining local and global features with deep neural networks. Bioinformatics 36(4), 1114\u20131120 (2020)","journal-title":"Bioinformatics"},{"issue":"1","key":"34_CR19","first-page":"1","volume":"5","author":"P Zhang","year":"2015","unstructured":"Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Label propagation prediction of drug-drug interactions based on clinical side effects. Sci. Rep. 5(1), 1\u201310 (2015)","journal-title":"Sci. Rep."},{"issue":"1","key":"34_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-016-1414-x","volume":"18","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., Li, X.: Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform. 18(1), 1\u201312 (2017)","journal-title":"BMC Bioinform."},{"issue":"19","key":"34_CR21","first-page":"49","volume":"19","author":"Y Zheng","year":"2018","unstructured":"Zheng, Y., Peng, H., Zhang, X., Zhao, Z., Yin, J., Li, J.: Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases. BMC Bioinform. 19(19), 49\u201359 (2018)","journal-title":"BMC Bioinform."},{"issue":"13","key":"34_CR22","doi-asserted-by":"publisher","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","volume":"34","author":"M Zitnik","year":"2018","unstructured":"Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), i457\u2013i466 (2018)","journal-title":"Bioinformatics"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91415-8_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T20:11:41Z","timestamp":1699819901000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91415-8_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030914141","9783030914158"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91415-8_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"18 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/alan.cs.gsu.edu\/isbra21\/?q=node\/1","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"135","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":"51","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":"38% - 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":"2.97","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":"2.95","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)"}}]}}