{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:16:32Z","timestamp":1742984192465,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031231971"},{"type":"electronic","value":"9783031231988"}],"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_24","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:36:12Z","timestamp":1672540572000},"page":"263-274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MPCDDI: A Secure Multiparty Computation-Based Deep Learning Framework for\u00a0Drug-Drug Interaction Predictions"],"prefix":"10.1007","author":[{"given":"Xia","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoliang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"24_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/3-540-46766-1_34","volume-title":"Advances in Cryptology \u2014 CRYPTO \u201991","author":"D Beaver","year":"1992","unstructured":"Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 420\u2013432. Springer, Heidelberg (1992). https:\/\/doi.org\/10.1007\/3-540-46766-1_34"},{"key":"24_CR2","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"},{"issue":"6","key":"24_CR3","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/nbt.4108","volume":"36","author":"H Cho","year":"2018","unstructured":"Cho, H., Wu, D.J., Berger, B.: Secure genome-wide association analysis using multiparty computation. Nat. Biotechnol. 36(6), 547\u2013551 (2018)","journal-title":"Nat. Biotechnol."},{"key":"24_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/978-3-540-30576-7_19","volume-title":"Theory of Cryptography","author":"R Cramer","year":"2005","unstructured":"Cramer, R., Damg\u00e5rd, I., Ishai, Y.: Share conversion, pseudorandom secret-sharing and applications to secure computation. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 342\u2013362. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-30576-7_19"},{"key":"24_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/978-3-642-32009-5_38","volume-title":"Advances in Cryptology \u2013 CRYPTO 2012","author":"I Damg\u00e5rd","year":"2012","unstructured":"Damg\u00e5rd, I., Pastro, V., Smart, N., Zakarias, S.: Multiparty computation from somewhat homomorphic encryption. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 643\u2013662. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32009-5_38"},{"issue":"D1","key":"24_CR6","doi-asserted-by":"publisher","first-page":"D1104","DOI":"10.1093\/nar\/gks994","volume":"41","author":"AP Davis","year":"2013","unstructured":"Davis, A.P., et al.: The comparative toxicogenomics database: update 2013. Nucleic Acids Res. 41(D1), D1104\u2013D1114 (2013)","journal-title":"Nucleic Acids Res."},{"key":"24_CR7","unstructured":"Deac, A., Huang, Y.H., Veli\u010dkovi\u0107, P., Li\u00f2, P., Tang, J.: Drug-drug adverse effect prediction with graph co-attention. arXiv preprint arXiv:1905.00534 (2019)"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Demmler, D., Schneider, T., Zohner, M.: ABY-a framework for efficient mixed-protocol secure two-party computation. In: NDSS (2015)","DOI":"10.14722\/ndss.2015.23113"},{"issue":"15","key":"24_CR9","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":"24_CR10","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"},{"issue":"6","key":"24_CR11","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1056\/NEJMra020526","volume":"348","author":"WE Evans","year":"2003","unstructured":"Evans, W.E., McLeod, H.L.: Pharmacogenomics-drug disposition, drug targets, and side effects. N. Engl. J. Med. 348(6), 538\u2013549 (2003)","journal-title":"N. Engl. J. Med."},{"issue":"7139","key":"24_CR12","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1038\/446975a","volume":"446","author":"KM Giacomini","year":"2007","unstructured":"Giacomini, K.M., Krauss, R.M., Roden, D.M., Eichelbaum, M., Hayden, M.R., Nakamura, Y.: When good drugs go bad. Nature 446(7139), 975\u2013977 (2007)","journal-title":"Nature"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game, or a completeness theorem for protocols with honest majority. In: Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio Micali, pp. 307\u2013328 (2019)","DOI":"10.1145\/3335741.3335755"},{"issue":"D1","key":"24_CR14","doi-asserted-by":"publisher","first-page":"D1113","DOI":"10.1093\/nar\/gkr912","volume":"40","author":"N Hecker","year":"2012","unstructured":"Hecker, N., et al.: SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Res. 40(D1), D1113\u2013D1117 (2012)","journal-title":"Nucleic Acids Res."},{"issue":"6412","key":"24_CR15","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1126\/science.aat4807","volume":"362","author":"B Hie","year":"2018","unstructured":"Hie, B., Cho, H., Berger, B.: Realizing private and practical pharmacological collaboration. Science 362(6412), 347\u2013350 (2018)","journal-title":"Science"},{"key":"24_CR16","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"issue":"6352","key":"24_CR17","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1126\/science.aam9710","volume":"357","author":"KA Jagadeesh","year":"2017","unstructured":"Jagadeesh, K.A., Wu, D.J., Birgmeier, J.A., Boneh, D., Bejerano, G.: Deriving genomic diagnoses without revealing patient genomes. Science 357(6352), 692\u2013695 (2017)","journal-title":"Science"},{"key":"24_CR18","unstructured":"Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., van der Maaten, L.: CRYPTEN: secure multi-party computation meets machine learning. In: Advances in Neural Information Processing Systems, vol. 34, pp. 4961\u20134973 (2021)"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Knox, C., et al.: DrugBank 3.0: a comprehensive resource for \u2018omics\u2019 research on drugs. Nucleic Acids Res. 39(suppl_1), D1035\u2013D1041 (2010)","DOI":"10.1093\/nar\/gkq1126"},{"issue":"1","key":"24_CR20","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1038\/msb.2009.98","volume":"6","author":"M Kuhn","year":"2010","unstructured":"Kuhn, M., Campillos, M., Letunic, I., Jensen, L.J., Bork, P.: A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(1), 343 (2010)","journal-title":"Mol. Syst. Biol."},{"issue":"1\u201379","key":"24_CR21","first-page":"4","volume":"1","author":"G Landrum","year":"2013","unstructured":"Landrum, G.: RDKit documentation. Release 1(1\u201379), 4 (2013)","journal-title":"Release"},{"key":"24_CR22","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":"1","key":"24_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-017-00680-8","volume":"8","author":"Y Luo","year":"2017","unstructured":"Luo, Y., et al.: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 8(1), 1\u201313 (2017)","journal-title":"Nat. Commun."},{"issue":"9","key":"24_CR24","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1093\/bioinformatics\/btaa038","volume":"36","author":"R Ma","year":"2020","unstructured":"Ma, R., et al.: Secure multiparty computation for privacy-preserving drug discovery. Bioinformatics 36(9), 2872\u20132880 (2020)","journal-title":"Bioinformatics"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Ma, T., Xiao, C., Zhou, J., Wang, F.: Drug similarity integration through attentive multi-view graph auto-encoders. arXiv preprint arXiv:1804.10850 (2018)","DOI":"10.24963\/ijcai.2018\/483"},{"issue":"D1","key":"24_CR26","doi-asserted-by":"publisher","first-page":"D1118","DOI":"10.1093\/nar\/gkt1129","volume":"42","author":"C Qin","year":"2014","unstructured":"Qin, C., et al.: Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res. 42(D1), D1118\u2013D1123 (2014)","journal-title":"Nucleic Acids Res."},{"key":"24_CR27","unstructured":"Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R.: struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385\u2013394 (2017)"},{"issue":"18","key":"24_CR28","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."},{"issue":"11","key":"24_CR29","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1145\/359168.359176","volume":"22","author":"A Shamir","year":"1979","unstructured":"Shamir, A.: How to share a secret. Commun. ACM 22(11), 612\u2013613 (1979)","journal-title":"Commun. ACM"},{"issue":"1","key":"24_CR30","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":"24_CR31","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"},{"issue":"9","key":"24_CR32","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. Protoc. 9(9), 2147\u20132163 (2014)","journal-title":"Nat. Protoc."},{"key":"24_CR33","doi-asserted-by":"crossref","unstructured":"Wang, X., Cheng, Y., Yang, Y., Li, F., Peng, S.: Multi-task joint strategies of self-supervised representation learning on biomedical networks for drug discovery. arXiv preprint arXiv:2201.04437 (2022)","DOI":"10.21203\/rs.3.rs-1260249\/v1"},{"issue":"4","key":"24_CR34","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1093\/bioinformatics\/btz718","volume":"36","author":"X Yue","year":"2020","unstructured":"Yue, X., et al.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241\u20131251 (2020)","journal-title":"Bioinformatics"},{"issue":"13","key":"24_CR35","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\/978-3-031-23198-8_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T18:05:29Z","timestamp":1689703529000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23198-8_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031231971","9783031231988"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23198-8_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}