{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T17:13:54Z","timestamp":1778087634461,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032253101","type":"print"},{"value":"9783032253118","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-25311-8_29","type":"book-chapter","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:35:04Z","timestamp":1778085304000},"page":"373-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Prediction for\u00a0Drug-Drug Interaction Using a\u00a0Knowledge Graph"],"prefix":"10.1007","author":[{"given":"Golnaz","family":"Taheri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahnaz","family":"Habibi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tahereh","family":"Sedghamiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1038\/s41467-019-09186-x","volume":"10","author":"F Cheng","year":"2019","unstructured":"Cheng, F., Kov\u00e1cs, I.A., Barab\u00e1si, A.L.: Network-based prediction of drug combinations. Nat. Commun. 10, 1197 (2019)","journal-title":"Nat. Commun."},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1016\/S1470-2045(18)30351-6","volume":"19","author":"AX Zhu","year":"2018","unstructured":"Zhu, A.X., Finn, R.S., Edeline, J., Cattan, S., Ogasawara, S., et al.: Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol. 19, 940\u2013952 (2018)","journal-title":"Lancet Oncol."},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Entacapone\/levodopa\/carbidopa combination tablet: stalevo. Drugs R&D 4, 310\u2013311 (2003)","DOI":"10.2165\/00126839-200304050-00006"},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1002\/cpt.1434","volume":"105","author":"J Niu","year":"2019","unstructured":"Niu, J., Straubinger, R.M., Mager, D.E.: Pharmacodynamic drug-drug interactions. Clin. Pharmacol. Ther. 105, 1395\u20131406 (2019)","journal-title":"Clin. Pharmacol. Ther."},{"key":"29_CR5","first-page":"1","volume":"13","author":"R Aghdam","year":"2021","unstructured":"Aghdam, R., Habibi, M., Taheri, G.: Using informative features in machine learning based method for COVID-19 drug repurposing. J. Chem. 13, 1\u20134 (2021)","journal-title":"J. Chem."},{"key":"29_CR6","doi-asserted-by":"publisher","first-page":"9378","DOI":"10.1038\/s41598-021-88427-w","volume":"11","author":"M Habibi","year":"2021","unstructured":"Habibi, M., Taheri, G., Aghdam, R.: A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing. Sci. Rep. 11, 9378 (2021)","journal-title":"Sci. Rep."},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s12859-016-1415-9","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, 18 (2017)","journal-title":"BMC Bioinform."},{"key":"29_CR8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196865","volume":"13","author":"A Kastrin","year":"2018","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, e0196865 (2018)","journal-title":"PLoS ONE"},{"key":"29_CR9","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12918-018-0532-7","volume":"12","author":"H Yu","year":"2018","unstructured":"Yu, H., Mao, K.T., Shi, J.Y., Huang, H., et al.: Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization. BMC Syst. Biol. 12, 101\u2013110 (2018)","journal-title":"BMC Syst. Biol."},{"key":"29_CR10","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ins.2019.05.017","volume":"497","author":"S Zhang","year":"2019","unstructured":"Zhang, S.: SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug\u2013drug interactions. Inf. Sci. 497, 189\u2013201 (2019)","journal-title":"Inf. Sci."},{"key":"29_CR11","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\u2013drug interaction prediction. Bioinformatics 32, 3175\u20133182 (2016)","journal-title":"Bioinformatics"},{"key":"29_CR12","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, 592 (2012)","journal-title":"Mol. Syst. Biol."},{"key":"29_CR13","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\u2013drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc. 21, e278\u2013e286 (2014)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"29_CR14","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1186\/s12859-020-03724-x","volume":"21","author":"YH Feng","year":"2020","unstructured":"Feng, Y.H., Zhang, S., Shi, J.: DPDDI: a deep predictor for drug-drug interactions. BMC Bioinform. 21, 419 (2020)","journal-title":"BMC Bioinform."},{"key":"29_CR15","doi-asserted-by":"publisher","first-page":"12339","DOI":"10.1038\/srep12339","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, 12339 (2015)","journal-title":"Sci. Rep."},{"issue":"7","key":"29_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0255270","volume":"16","author":"M Habibi","year":"2021","unstructured":"Habibi, M., Taheri, G.: Topological network based drug repurposing for coronavirus 2019. PLoS ONE 16(7), e0255270 (2021)","journal-title":"PLoS ONE"},{"key":"29_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. NN Learn. Syst. 32, 4\u201324 (2020)","journal-title":"IEEE Trans. NN Learn. Syst."},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"15141","DOI":"10.1038\/s41598-023-42127-9","volume":"13","author":"G Taheri","year":"2023","unstructured":"Taheri, G., Habibi, M.: Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms. Sci. Rep. 13, 15141 (2023)","journal-title":"Sci. Rep."},{"key":"29_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008631","volume":"17","author":"L Han","year":"2021","unstructured":"Han, L., Sayyid, Z.N., Altman, R.B.: Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease. PLoS Comput. Biol. 17, e1008631 (2021)","journal-title":"PLoS Comput. Biol."},{"key":"29_CR20","first-page":"27","volume":"35","author":"J Yang","year":"2018","unstructured":"Yang, J., Li, A., Li, Y., Guo, X., Wang, M.: A novel approach for drug response prediction in cancer cell lines via network representation learning. Bioinformatics 35, 27\u20131535 (2018)","journal-title":"Bioinformatics"},{"key":"29_CR21","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1016\/j.jmb.2018.06.041","volume":"430","author":"DH Le","year":"2018","unstructured":"Le, D.H., Pham, V.H.: Drug response prediction by globally capturing drug and cell line information in a heterogeneous network. J. Mol. Biol. 430, 2993\u20133004 (2018)","journal-title":"J. Mol. Biol."},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Wang, F., Lei, X., Liao, B., Wu, F.X.: Predicting drug\u2013drug interactions by graph convolutional network with multi-kernel. Brief. Bioinform. 23 (2021)","DOI":"10.1093\/bib\/bbab511"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, Z., Qiu, Y., Chen, Y.P., Zhang, W.: Structural network embedding using multi-modal deep auto-encoders for predicting drug-drug interactions. In: 2019 IEEE International Conference, BIBM, pp. 445\u2013450 (2019)","DOI":"10.1109\/BIBM47256.2019.8983337"},{"key":"29_CR24","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1038\/s41467-021-21997-5","volume":"12","author":"P Jia","year":"2021","unstructured":"Jia, P., Hu, R., Pei, G., Dai, Y., et al.: Deep generative neural network for accurate drug response imputation. Nat. Commun. 12, 1740 (2021)","journal-title":"Nat. Commun."},{"key":"29_CR25","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1038\/s41467-021-22170-8","volume":"12","author":"H Gerdes","year":"2021","unstructured":"Gerdes, H., Casado, P., Dokal, A., et al.: Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat. Commun. 12, 1850 (2021)","journal-title":"Nat. Commun."},{"key":"29_CR26","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart, D.S., Feunang, Y.D., Guo, A.C., Lo, E.J., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074\u2013D1082 (2018)","journal-title":"Nucleic Acids Res."},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Chandrasekaran, B., Abed, S.N., Al-Attraqchi, O., Kuche, K., Tekade, R.K.: Computer-aided prediction of pharmacokinetic (ADMET) properties. In: Dosage form Design Para, pp. 731\u2013755 (2018)","DOI":"10.1016\/B978-0-12-814421-3.00021-X"},{"key":"29_CR28","first-page":"1","volume":"16","author":"T Bohnert","year":"2011","unstructured":"Bohnert, T., Prakash, C.: ADME profiling in drug discovery and development: an overview. Encyclopedia Drug Metabolism Interact. 16, 1\u201342 (2011)","journal-title":"Encyclopedia Drug Metabolism Interact."},{"key":"29_CR29","unstructured":"Zitnik, M., Sosic, R., Leskovec, J.: BioSNAP datasets: stanford biomedical network dataset collection. 5 (2018). http:\/\/snap.stanford.edu\/biodata"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-25311-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:35:07Z","timestamp":1778085307000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-25311-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032253101","9783032253118"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-25311-8_29","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}