{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:15:24Z","timestamp":1780596924505,"version":"3.54.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031231971","type":"print"},{"value":"9783031231988","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-23198-8_25","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:36:12Z","timestamp":1672540572000},"page":"275-285","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Multimodal Data Fusion-Based Deep Learning Approach for\u00a0Drug-Drug Interaction Prediction"],"prefix":"10.1007","author":[{"given":"An","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoliang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Cami, A., Manzi, S., Arnold, A., Reis, B.Y.: Pharmacointeraction network models predict unknown drug-drug interactions. PloS One 8(4), e61468 (2013)","DOI":"10.1371\/journal.pone.0061468"},{"issue":"1","key":"25_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00445-4","volume":"12","author":"A Capecchi","year":"2020","unstructured":"Capecchi, A., Probst, D., Reymond, J.-L.: One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. J. Cheminform. 12(1), 1\u201315 (2020). https:\/\/doi.org\/10.1186\/s13321-020-00445-4","journal-title":"J. Cheminform."},{"issue":"e2","key":"25_CR3","doi-asserted-by":"publisher","first-page":"e278","DOI":"10.1136\/amiajnl-2013-002512","volume":"21","author":"F Cheng","year":"2014","unstructured":"Cheng, F., Zhao, Z.: Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc. 21(e2), e278\u2013e286 (2014)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Deng, Y., et al.: Meta-DDIE: predicting drug-drug interaction events with few-shot learning. Briefings Bioinform. 23(1), bbab514 (2022)","DOI":"10.1093\/bib\/bbab514"},{"issue":"15","key":"25_CR5","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"},{"key":"25_CR6","unstructured":"Feeney, A., et al.: Relation matters in sampling: a scalable multi-relational graph neural network for drug-drug interaction prediction. arXiv preprint arXiv:2105.13975 (2021)"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Feng, Y.Y., Yu, H., Feng, Y.H., Shi, J.Y.: Directed graph attention networks for predicting asymmetric drug-drug interactions. Brief. Bioinform. 23(3) (2022)","DOI":"10.1093\/bib\/bbac151"},{"key":"25_CR8","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.jbi.2017.04.021","volume":"70","author":"R Ferdousi","year":"2017","unstructured":"Ferdousi, R., Safdari, R., Omidi, Y.: Computational prediction of drug-drug interactions based on drugs functional similarities. J. Biomed. Inform. 70, 54\u201364 (2017)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"25_CR9","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1038\/msb.2012.26","volume":"8","author":"A Gottlieb","year":"2012","unstructured":"Gottlieb, A., Stein, G.Y., Oron, Y., Ruppin, E., Sharan, R.: Indi: a computational framework for inferring drug interactions and their associated recommendations. Mol. Syst. Biol. 8(1), 592 (2012)","journal-title":"Mol. Syst. Biol."},{"key":"25_CR10","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"},{"issue":"1","key":"25_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-022-00589-5","volume":"14","author":"E Kim","year":"2022","unstructured":"Kim, E., Nam, H.: Deside-ddi: interpretable prediction of drug-drug interactions using drug-induced gene expressions. J. Cheminform. 14(1), 1\u201312 (2022)","journal-title":"J. Cheminform."},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Lin, X., Quan, Z., Wang, Z.J., Ma, T., Zeng, X.: KGNN: knowledge graph neural network for drug-drug interaction prediction. In: IJCAI, vol. 380, pp. 2739\u20132745 (2020)","DOI":"10.24963\/ijcai.2020\/380"},{"issue":"2","key":"25_CR13","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1080\/07391102.2016.1138142","volume":"35","author":"L Liu","year":"2017","unstructured":"Liu, L., et al.: Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection. J. Biomol. Struct. Dyn. 35(2), 312\u2013329 (2017)","journal-title":"J. Biomol. Struct. Dyn."},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Lyu, T., Gao, J., Tian, L., Li, Z., Zhang, P., Zhang, J.: MDNN: a multimodal deep neural network for predicting drug-drug interaction events. In: International Joint Conferences on Artifical Intelligence (2022)","DOI":"10.24963\/ijcai.2021\/487"},{"issue":"1","key":"25_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-97193-8","volume":"11","author":"S Mei","year":"2021","unstructured":"Mei, S., Zhang, K.: A machine learning framework for predicting drug-drug interactions. Sci. Rep. 11(1), 1\u201312 (2021)","journal-title":"Sci. Rep."},{"key":"25_CR16","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Icml (2010)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Nyamabo, A.K., Yu, H., Liu, Z., Shi, J.Y.: Drug-drug interaction prediction with learnable size-adaptive molecular substructures. Brief. Bioinform. 23(1), bbab441 (2022)","DOI":"10.1093\/bib\/bbab441"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Park, K., Kim, D., Ha, S., Lee, D.: Predicting pharmacodynamic drug-drug interactions through signaling propagation interference on protein-protein interaction networks. PloS one 10(10), e0140816 (2015)","DOI":"10.1371\/journal.pone.0140816"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Zhang, Y., Deng, Y., Liu, S., Zhang, W.: A comprehensive review of computational methods for drug-drug interaction detection. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2021)","DOI":"10.1109\/TCBB.2021.3081268"},{"issue":"6","key":"25_CR20","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","volume":"34","author":"D Ramachandram","year":"2017","unstructured":"Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Sign. Process. Mag. 34(6), 96\u2013108 (2017)","journal-title":"IEEE Sign. Process. Mag."},{"issue":"18","key":"25_CR21","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. Natl. Acad. Sci. 115(18), E4304\u2013E4311 (2018)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Shen, Y., et al.: Drug2vec: knowledge-aware feature-driven method for drug representation learning. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 757\u2013800. IEEE (2018)","DOI":"10.1109\/BIBM.2018.8621390"},{"issue":"12","key":"25_CR23","first-page":"1","volume":"18","author":"JY Shi","year":"2017","unstructured":"Shi, J.Y., Li, J.X., Gao, K., Lei, P., Yiu, S.M.: Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features. BMC Bioinform. 18(12), 1\u20139 (2017)","journal-title":"BMC Bioinform."},{"key":"25_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.11.002","volume":"168","author":"JY Shi","year":"2019","unstructured":"Shi, J.Y., et al.: Predicting combinative drug pairs via multiple classifier system with positive samples only. Comput. Meth. Prog. Biomed. 168, 1\u201310 (2019)","journal-title":"Comput. Meth. Prog. Biomed."},{"issue":"20","key":"25_CR25","doi-asserted-by":"publisher","first-page":"3175","DOI":"10.1093\/bioinformatics\/btw342","volume":"32","author":"D Sridhar","year":"2016","unstructured":"Sridhar, D., Fakhraei, S., Getoor, L.: A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinformatics 32(20), 3175\u20133182 (2016)","journal-title":"Bioinformatics"},{"issue":"1","key":"25_CR26","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."},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Stahlschmidt, S.R., Ulfenborg, B., Synnergren, J.: Multimodal deep learning for biomedical data fusion: a review. Brief. Bioinform. 23(2), bbab569 (2022)","DOI":"10.1093\/bib\/bbab569"},{"issue":"11","key":"25_CR28","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1021\/ci200367w","volume":"51","author":"M Takarabe","year":"2011","unstructured":"Takarabe, M., Shigemizu, D., Kotera, M., Goto, S., Kanehisa, M.: Network-based analysis and characterization of adverse drug-drug interactions. J. Chem. Inf. Model. 51(11), 2977\u20132985 (2011)","journal-title":"J. Chem. Inf. Model."},{"issue":"9","key":"25_CR29","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1038\/nprot.2014.151","volume":"9","author":"S Vilar","year":"2014","unstructured":"Vilar, S., et al.: Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat. Protocols 9(9), 2147\u20132163 (2014)","journal-title":"Nat. Protocols"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Wishart, D.S., et al.: Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic Acids Res. 46(D1), D1074\u2013D1082 (2018)","DOI":"10.1093\/nar\/gkx1037"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Chapter 10 - deep learning. In: Data Mining 4th edn., pp. 417\u2013466. Morgan Kaufmann, (2017)","DOI":"10.1016\/B978-0-12-804291-5.00010-6"},{"issue":"1","key":"25_CR32","first-page":"101","volume":"12","author":"H Yu","year":"2018","unstructured":"Yu, H., et al.: Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization. BMC Syst. Biol. 12(1), 101\u2013110 (2018)","journal-title":"BMC Syst. Biol."},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zang, T.: CNN-DDI: a novel deep learning method for predicting drug-drug interactions. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1708\u20131713. IEEE (2020)","DOI":"10.1109\/BIBM49941.2020.9313404"},{"issue":"1","key":"25_CR34","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."},{"key":"25_CR35","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jbi.2018.11.005","volume":"88","author":"W Zhang","year":"2018","unstructured":"Zhang, W., Chen, Y., Li, D., Yue, X.: Manifold regularized matrix factorization for drug-drug interaction prediction. J. Biomed. Inform. 88, 90\u201397 (2018)","journal-title":"J. Biomed. Inform."},{"issue":"7","key":"25_CR36","doi-asserted-by":"publisher","first-page":"2820","DOI":"10.1109\/JBHI.2020.3048059","volume":"25","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Liu, Y., Zhang, Y., Li, D.: Attribute supervised probabilistic dependent matrix tri-factorization model for the prediction of adverse drug-drug interaction. IEEE J. Biomed. Health Inform. 25(7), 2820\u20132832 (2020)","journal-title":"IEEE J. Biomed. Health Inform."}],"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\/978-3-031-23198-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T18:05:13Z","timestamp":1689703513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23198-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031231971","9783031231988"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23198-8_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","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":"Haifa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mangul-lab-usc.github.io\/ISBRA","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":"72","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":"30","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":"4","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":"42% - 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":"3","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":"5","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)"}}]}}