{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T22:54:10Z","timestamp":1761519250264,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T00:00:00Z","timestamp":1552521600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602013"],"award-info":[{"award-number":["61602013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Key Fundamental Research Projects","award":["JCYJ20170818091546869"],"award-info":[{"award-number":["JCYJ20170818091546869"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s13321-019-0342-y","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T13:03:24Z","timestamp":1552568604000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["KMR: knowledge-oriented medicine representation learning for drug\u2013drug interaction and similarity computation"],"prefix":"10.1186","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3220-904X","authenticated-orcid":false,"given":"Ying","family":"Shen","sequence":"first","affiliation":[]},{"given":"Kaiqi","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Buzhou","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Yaliang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Du","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Lei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,14]]},"reference":[{"key":"342_CR1","doi-asserted-by":"crossref","unstructured":"Zhou J, Yuan L, Liu J, Ye J (2011) A multi-task learning formulation for predicting disease progression. In: the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, USA, 21\u201324 Aug 2011, pp 814-822","DOI":"10.1145\/2020408.2020549"},{"key":"342_CR2","doi-asserted-by":"crossref","unstructured":"Shen Y, Deng Y, Yang M, Li Y, Du N, Fan W, Lei K.\u00a0(2018) Knowledge-aware attentive neural network for ranking question answer pairs. In: the 41st international ACM SIGIR conference on research & development in information retrieval, Ann Arbor, MI, USA, 08\u201312 July 2018, pp 901\u2013904","DOI":"10.1145\/3209978.3210081"},{"key":"342_CR3","unstructured":"Severyn A, Nicosia M, Moschitti A (2013) Learning semantic textual similarity with structural representations. In: the 51st annual meeting of the Association for Computational Linguistics, Sofia, Bulgaria, 4\u20139 Aug 2013, vol 2, pp 714\u2013718"},{"key":"342_CR4","unstructured":"Yang M, Qu Q, Shen Y, Liu Q, Zhao W, Zhu J (2018) Aspect and sentiment aware abstractive review summarization. COLING 2018. COLING: Santa Fe, New Mexico, USA, 20\u201326 Aug 2018"},{"key":"342_CR5","unstructured":"Zhang C, Li Y, Du N, Fan W, Yu P (2018) On the generative discovery of structured medical knowledge. In: the 24th ACM SIGKDD international conference on knowledge discovery & data mining. London, UK, 19\u201323 Aug 2018, pp 2720\u20132728"},{"key":"342_CR6","doi-asserted-by":"crossref","unstructured":"Choi E, Bahadori M T, Searles E, Coffey C, Thompson M (2016) Multi-layer representation learning for medical concept. In: the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, 13\u201317 Aug 2016, pp 1495-1504","DOI":"10.1145\/2939672.2939823"},{"issue":"2","key":"342_CR7","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.cmpb.2014.08.009","volume":"117","author":"S Korkmaz","year":"2014","unstructured":"Korkmaz S, Zararsiz G, Goksuluk D (2014) Drug\/nondrug classification using support vector machines with various feature selection strategies. Comput Methods Programs Biomed 117(2):51\u201360","journal-title":"Comput Methods Programs Biomed"},{"key":"342_CR8","unstructured":"Che Z, Cheng Y, Sun Z, Liu Y (2016) Exploiting convolutional neural network for risk prediction with medical feature embedding. In: NIPS workshop on machine learning for health (NIPS-ML4HC), Barcelona, Spain, 05\u201310, Dec 2016"},{"issue":"4","key":"342_CR9","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TKDE.2003.1209005","volume":"15","author":"Y Li","year":"2003","unstructured":"Li Y, Bandar Z, McLean D (2003) An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans Knowl Data Eng 15(4):871\u2013882","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"342_CR10","unstructured":"Resnik P (2005) Using information content to evaluate semantic similarity in a taxonomy. In: IJCAI, Edinburgh, Scotland, UK, 30 July\u20135 Aug, pp 448\u2013453"},{"key":"342_CR11","doi-asserted-by":"crossref","unstructured":"Choi E, Bahadori M T, Song L, Stewart W, Sun J (2017) GRAM: graph-based attention model for healthcare representation learning. In: the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, Halifax, NS, Canada, 13\u201317 Aug 2017, pp 787\u2013795","DOI":"10.1145\/3097983.3098126"},{"key":"342_CR12","unstructured":"Turian J, Ratinov L, Bengio Y (2010) Word representations: a simple and general method for semi-supervised learning. In: the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics. Uppsala, Sweden, 11\u201316 July 2010, pp 384\u2013394"},{"key":"342_CR13","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"342_CR14","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1613\/jair.2934","volume":"37","author":"PD Turney","year":"2010","unstructured":"Turney PD, Pantel P (2010) From frequency to meaning: vector space models of semantics. J Artif Intell Res 37:141\u2013188","journal-title":"J Artif Intell Res"},{"key":"342_CR15","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems. Lake Tahoe, 05\u201310 Dec 2013, pp 3111\u20133119"},{"key":"342_CR16","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, Lake Tahoe, 05\u201310 Dec 2013, pp 2787\u20132795"},{"key":"342_CR17","doi-asserted-by":"crossref","unstructured":"Ji G, He S, Xu L, Zhao J (2015) Knowledge graph embedding via dynamic mapping matrix. In: the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, Beijing, China, 16\u201321 Aug 2015, pp 687\u2013696","DOI":"10.3115\/v1\/P15-1067"},{"key":"342_CR18","doi-asserted-by":"crossref","unstructured":"Ji G, Liu K, He S, Zhao J (2016) Knowledge graph completion with adaptive sparse transfer matrix. In: AAAI 2016, Phoenix, AZ, USA, 12\u201317 Feb 2016, pp 985\u2013991","DOI":"10.1609\/aaai.v30i1.10089"},{"issue":"1","key":"342_CR19","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s10994-010-5205-8","volume":"81","author":"N Lao","year":"2010","unstructured":"Lao N, Cohen WW (2010) Relational retrieval using a combination of path-constrained random walks. Mach Learn 81(1):53\u201367","journal-title":"Mach Learn"},{"key":"342_CR20","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, USA, 24\u201327 Aug 2014, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"342_CR21","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, 13\u201317 Aug 2016, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"342_CR22","unstructured":"Socher R, Huval B, Manning C D, Ng A (2012) Semantic compositionality through recursive matrix-vector spaces. In: the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Jeju Island, Korea, 12\u201314 July 2012, pp 1201\u20131211"},{"key":"342_CR23","unstructured":"Wang T, Wu D J, Coates A, Ng A (2012) End-to-end text recognition with convolutional neural networks. In: the 21st international conference on pattern recognition (ICPR), Tsukuba, Japan, 11\u201315 Nov 2012, pp 3304\u20133308"},{"key":"342_CR24","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: ICLR 2015, San Diego, CA, 7\u20139 May 2015"},{"key":"342_CR25","unstructured":"Sukhbaatar S, Weston J, Fergus R (2015) End-to-end memory networks. In: Advances in neural information processing systems, Montreal, Canada, 07\u201312 Dec 2015, pp 2440-2448"},{"key":"342_CR26","doi-asserted-by":"crossref","unstructured":"Vinyals O, Toshev A, Bengio S, Erhan, D (2015) Show and tell: a neural image caption generator. In: the IEEE conference on computer vision and pattern recognition (CVPR 2015), Boston, USA, 7\u201312 June 2015, pp 3156\u20133164","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"342_CR27","doi-asserted-by":"publisher","first-page":"D668","DOI":"10.1093\/nar\/gkj067","volume":"34","author":"DS Wishart","year":"2006","unstructured":"Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668\u2013D672","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"342_CR28","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1186\/s12859-018-2167-5","volume":"19","author":"S Sang","year":"2018","unstructured":"Sang S, Yang Z, Wang L, Liu X, Lin H, Wang J (2018) SemaTyP: a knowledge graph based literature mining method for drug discovery. BMC Bioinform 19(1):193","journal-title":"BMC Bioinform"},{"key":"342_CR29","first-page":"584","volume":"205","author":"JA Minarro-Gim\u00e9nez","year":"2014","unstructured":"Minarro-Gim\u00e9nez JA, Marin-Alonso O (2014) Samwald M. Exploring the application of deep learning techniques on medical text corpora. Stud Health Technol Inform 205:584\u2013588","journal-title":"Stud Health Technol Inform"},{"key":"342_CR30","doi-asserted-by":"crossref","unstructured":"De Vine L, Zuccon G, Koopman B, Sitbon L, Bruza P (2014) Medical semantic similarity with a neural language model. In: the 23rd ACM international conference on information and knowledge management (CIKM), Lingotto, Italy, 22\u201326 Oct 2018, pp 1819\u20131822","DOI":"10.1145\/2661829.2661974"},{"issue":"D1","key":"342_CR31","doi-asserted-by":"publisher","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","volume":"44","author":"M Kuhn","year":"2015","unstructured":"Kuhn M, Letunic I, Jensen LJ, Bork P (2015) The SIDER database of drugs and side effects. Nucleic Acids Res 44(D1):D1075\u2013D1079","journal-title":"Nucleic Acids Res"},{"key":"342_CR32","unstructured":"Mahoney A, Evans J (2008) Comparing drug classification systems. In: AMIA annual symposium proceedings, Washington, DC, 8\u201312 Nov 2008, pp 1039\u20131039"},{"issue":"1","key":"342_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13321-016-0174-y","volume":"8","author":"YD Feunang","year":"2016","unstructured":"Feunang YD, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Greiner R (2016) ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform 8(1):61","journal-title":"J Cheminform"},{"key":"342_CR34","doi-asserted-by":"crossref","unstructured":"Tai K S, Socher R, Manning C D (2015) Improved semantic representations from tree-structured long short-term memory networks. In: ACL 2015, Beijing, China, 26\u201331 July 2015","DOI":"10.3115\/v1\/P15-1150"},{"key":"342_CR35","doi-asserted-by":"crossref","unstructured":"Dyer C, Ballesteros M, Ling W, Matthews A, Smith NA (2015) Transition-based dependency parsing with stack long short-term memory. In: ACL 2015, Beijing, China, 26\u201331 July 2015","DOI":"10.3115\/v1\/P15-1033"},{"issue":"3","key":"342_CR36","first-page":"69","volume":"24","author":"M Mukaka","year":"2012","unstructured":"Mukaka M (2012) A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24(3):69\u201371","journal-title":"Malawi Med J"},{"issue":"2","key":"342_CR37","doi-asserted-by":"publisher","first-page":"87","DOI":"10.2478\/v10117-011-0021-1","volume":"30","author":"J Hauke","year":"2011","unstructured":"Hauke J, Kossowski T (2011) Comparison of values of Pearson\u2019s and Spearman\u2019s correlation coefficients on the same sets of data. Quaest Geogr 30(2):87\u201393","journal-title":"Quaest Geogr"},{"key":"342_CR38","unstructured":"Pu Y, Gan Z, Henao R, Yuan X, Li C, Stevens A (2016) Variational autoencoder for deep learning of images, labels and caption. In: Advances in neural information processing systems, Barcelona, Spain, 4\u20139 Dec 2016, pp 2352\u20132360"},{"key":"342_CR39","unstructured":"Bj\u00f6rne J, Kaewphan S, Salakoski T (2013) UTurku: drug named entity recognition and drug-drug interaction extraction using SVM classification and domain knowledge. In: Second joint conference on lexical and computational semantics and the seventh international workshop on semantic evaluation (SemEval 2013). Atlanta, GA, 14\u201315 June 2013, vol 2, pp 651\u2013659"},{"key":"342_CR40","unstructured":"Chowdhury MFM, Lavelli A (2013) FBK-irst: a multi-phase kernel based approach for drug-drug interaction detection and classification that exploits linguistic information. In: Second joint conference on lexical and computational semantics and the seventh international workshop on semantic evaluation (SemEval 2013), Atlanta, GA, 14\u201315 June 2013, vol 2, pp 351\u2013355"},{"key":"342_CR41","doi-asserted-by":"crossref","unstructured":"Liu S, Tang B, Chen Q, Wang X (2016) Drug-drug interaction extraction via convolutional neural networks. Comput Math Methods Med 2016","DOI":"10.1155\/2016\/6918381"},{"issue":"1","key":"342_CR42","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1186\/s12859-017-1855-x","volume":"18","author":"W Zheng","year":"2017","unstructured":"Zheng W, Lin H, Luo L, Zhao Z, Li Z, Zhang Y, Wang J (2017) An attention-based effective neural model for drug-drug interactions extraction. BMC Bioinform 18(1):445","journal-title":"BMC Bioinform"},{"key":"342_CR43","unstructured":"Abdelaziz I, Fokoue A, Hassanzadeh O, Zhang P, Sadoghi M (2017) Large-scale structural and textual similarity-based mining of knowledge graph to predict drug\u2013drug interactions. In: Web semantics: science, services and agents on the world wide web, Perth, Australia, 3\u20137 Apr 2017, vol 44, pp 104\u2013117"},{"key":"342_CR44","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 (2015) Label propagation prediction of drug-drug interactions based on clinical side effects. Sci Rep 5:12339","journal-title":"Sci Rep"},{"key":"342_CR45","unstructured":"Zeng D, Liu K, Lai S, Zhou G, Zhao J (2014) Relation classification via convolutional deep neural network. In: COLING 2014, the 25th international conference on computational linguistics: technical papers, Dublin, Ireland, 23rd Aug 2014, pp 2335\u20132344"},{"key":"342_CR46","unstructured":"Zeng D, Liu K, Chen Y, Zhao J (2015) Distant supervision for relation extraction via piecewise convolutional neural networks. In: the 2015 conference on empirical methods in natural language processing. Lisbon, Portugal, 17\u201321 Sept 2015, pp 1753\u20131762"},{"key":"342_CR47","unstructured":"Ho P L, Wong S S Y (2012) Reducing bacterial resistance with IMPACT-Interhospital multi-disciplinary programme on antimicrobial chemo therapy, 4th edn. Centre for Health Protection"},{"issue":"3","key":"342_CR48","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.jbi.2006.06.004","volume":"40","author":"T Pedersen","year":"2007","unstructured":"Pedersen T, Pakhomov SV, Patwardhan S, Chute CG (2007) Measures of semantic similarity and relatedness in the biomedical domain. J Biomed Inform 40(3):288\u2013299","journal-title":"J Biomed Inform"},{"issue":"3","key":"342_CR49","doi-asserted-by":"publisher","first-page":"55","DOI":"10.4018\/jswis.2006070104","volume":"2","author":"A Hliaoutakis","year":"2006","unstructured":"Hliaoutakis A, Varelas G, Voutsakis E, Petrakis EG, Milios E (2006) Information retrieval by semantic similarity. Int J Semant Web and Inf Syst (IJSWIS) 2(3):55\u201373","journal-title":"Int J Semant Web and Inf Syst (IJSWIS)"},{"key":"342_CR50","doi-asserted-by":"crossref","unstructured":"Traverso I, Vidal M E, K\u00e4mpgen B, Sure-Vetter Y (2016) GADES: a graph-based semantic similarity measure. In: the 12th international conference on semantic systems. ACM, pp 101\u2013104","DOI":"10.1145\/2993318.2993343"},{"issue":"1","key":"342_CR51","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TKDE.2016.2610428","volume":"29","author":"G Zhu","year":"2017","unstructured":"Zhu G, Iglesias C (2017) Computing semantic similarity of concepts in knowledge graphs. IEEE Trans Knowl Data Eng 29(1):72\u201385","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0342-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-019-0342-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0342-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T20:27:08Z","timestamp":1663100828000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-019-0342-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,14]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["342"],"URL":"https:\/\/doi.org\/10.1186\/s13321-019-0342-y","relation":{},"ISSN":["1758-2946"],"issn-type":[{"type":"electronic","value":"1758-2946"}],"subject":[],"published":{"date-parts":[[2019,3,14]]},"assertion":[{"value":"17 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"22"}}