{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:33:10Z","timestamp":1742913190971,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319712451"},{"type":"electronic","value":"9783319712468"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"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":[[2017]]},"DOI":"10.1007\/978-3-319-71246-8_20","type":"book-chapter","created":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T09:03:20Z","timestamp":1514538200000},"page":"322-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["ALADIN: A New Approach for Drug\u2013Target Interaction Prediction"],"prefix":"10.1007","author":[{"given":"Krisztian","family":"Buza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ladislav","family":"Peska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,30]]},"reference":[{"issue":"1","key":"20_CR1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/nrg2918","volume":"12","author":"AL Barab\u00e1si","year":"2011","unstructured":"Barab\u00e1si, A.L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12(1), 56\u201368 (2011)","journal-title":"Nat. Rev. Genet."},{"unstructured":"Besemann, C., Denton, A., Yekkirala, A.: Differential association rule mining for the study of protein-protein interaction networks. In: 4th International Conference on Data Mining in Bioinformatics, pp. 72\u201380. Springer, Heidelberg (2004). https:\/\/dl.acm.org\/citation.cfm?id=3000590","key":"20_CR2"},{"key":"20_CR3","first-page":"687","volume":"11","author":"G Biau","year":"2010","unstructured":"Biau, G., C\u00e9rou, F., Guyader, A.: On the rate of convergence of the bagged nearest neighbor estimate. J. Mach. Learn. Res. 11, 687\u2013712 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"18","key":"20_CR4","doi-asserted-by":"publisher","first-page":"2397","DOI":"10.1093\/bioinformatics\/btp433","volume":"25","author":"K Bleakley","year":"2009","unstructured":"Bleakley, K., Yamanishi, Y.: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18), 2397\u20132403 (2009)","journal-title":"Bioinformatics"},{"key":"20_CR5","first-page":"25","volume":"52","author":"B Bolgar","year":"2016","unstructured":"Bolgar, B., Antal, P.: Bayesian matrix factorization with non-random missing data using informative Gaussian process priors and soft evidences. J. Mach. Learn. Res. 52, 25\u201336 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.knosys.2015.06.010","volume":"86","author":"K Buza","year":"2015","unstructured":"Buza, K., Nanopoulos, A., Nagy, G.: Nearest neighbor regression in the presence of bad hubs. Knowl.-Based Syst. 86, 250\u2013260 (2015)","journal-title":"Knowl.-Based Syst."},{"key":"20_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/978-3-642-12116-6_46","volume-title":"Computational Linguistics and Intelligent Text Processing","author":"P Cellier","year":"2010","unstructured":"Cellier, P., Charnois, T., Plantevit, M.: Sequential patterns to discover and characterise biological relations. In: Gelbukh, A. (ed.) CICLing 2010. LNCS, vol. 6008, pp. 537\u2013548. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12116-6_46"},{"issue":"7","key":"20_CR8","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.1039\/c2mb00002d","volume":"8","author":"X Chen","year":"2012","unstructured":"Chen, X., Liu, M.X., Yan, G.Y.: Drug-target interaction prediction by random walk on the heterogeneous network. Mol. BioSyst. 8(7), 1970\u20131978 (2012)","journal-title":"Mol. BioSyst."},{"issue":"1","key":"20_CR9","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/nbt1273","volume":"25","author":"AC Cheng","year":"2007","unstructured":"Cheng, A.C., Coleman, R.G., Smyth, K.T., Cao, Q., Soulard, P., Caffrey, D.R., Salzberg, A.C., Huang, E.S.: Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 25(1), 71\u201375 (2007)","journal-title":"Nat. Biotechnol."},{"issue":"5","key":"20_CR10","doi-asserted-by":"publisher","first-page":"e1002503","DOI":"10.1371\/journal.pcbi.1002503","volume":"8","author":"F Cheng","year":"2012","unstructured":"Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Zhou, W., Huang, J., Tang, Y.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), e1002503 (2012)","journal-title":"PLoS Comput. Biol."},{"doi-asserted-by":"crossref","unstructured":"Davis, J., Santos Costa, V., Ray, S., Page, D.: An integrated approach to feature invention and model construction for drug activity prediction. In: Proceedings of the 24th International Conference on Machine Learning, pp. 217\u2013224 (2007)","key":"20_CR11","DOI":"10.1145\/1273496.1273524"},{"issue":"11","key":"20_CR12","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1038\/nbt.1990","volume":"29","author":"MI Davis","year":"2011","unstructured":"Davis, M.I., Hunt, J.P., Herrgard, S., Ciceri, P., Wodicka, L.M., Pallares, G., Hocker, M., Treiber, D.K., Zarrinkar, P.P.: Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29(11), 1046\u20131051 (2011)","journal-title":"Nat. Biotechnol."},{"issue":"1","key":"20_CR13","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1186\/1471-2105-10-374","volume":"10","author":"T Fayruzov","year":"2009","unstructured":"Fayruzov, T., De Cock, M., Cornelis, C., Hoste, V.: Linguistic feature analysis for protein interaction extraction. BMC Bioinform. 10(1), 374 (2009)","journal-title":"BMC Bioinform."},{"issue":"18","key":"20_CR14","doi-asserted-by":"publisher","first-page":"2304","DOI":"10.1093\/bioinformatics\/bts360","volume":"28","author":"M G\u00f6nen","year":"2012","unstructured":"G\u00f6nen, M.: Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics 28(18), 2304\u20132310 (2012)","journal-title":"Bioinformatics"},{"issue":"4","key":"20_CR15","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1002\/prot.10115","volume":"47","author":"I Halperin","year":"2002","unstructured":"Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins: Struct. Func. Bioinform. 47(4), 409\u2013443 (2002)","journal-title":"Proteins: Struct. Func. Bioinform."},{"issue":"39","key":"20_CR16","doi-asserted-by":"publisher","first-page":"11853","DOI":"10.1021\/ja036030u","volume":"125","author":"M Hattori","year":"2003","unstructured":"Hattori, M., Okuno, Y., Goto, S., Kanehisa, M.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125(39), 11853\u201311865 (2003)","journal-title":"J. Am. Chem. Soc."},{"issue":"1","key":"20_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.healthpol.2010.12.002","volume":"100","author":"S Morgan","year":"2011","unstructured":"Morgan, S., Grootendorst, P., Lexchin, J., Cunningham, C., Greyson, D.: The cost of drug development: a systematic review. Health Policy 100(1), 4\u201317 (2011)","journal-title":"Health Policy"},{"key":"20_CR18","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.apenergy.2014.04.077","volume":"129","author":"C Hu","year":"2014","unstructured":"Hu, C., Jain, G., Zhang, P., Schmidt, C., Gomadam, P., Gorka, T.: Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Appl. Energy 129, 49\u201355 (2014)","journal-title":"Appl. Energy"},{"issue":"5","key":"20_CR19","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.drudis.2016.01.007","volume":"21","author":"AA Jamali","year":"2016","unstructured":"Jamali, A.A., Ferdousi, R., Razzaghi, S., Li, J., Safdari, R., Ebrahimie, E.: Drugminer: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discov. Today 21(5), 718\u2013724 (2016)","journal-title":"Drug Discov. Today"},{"key":"20_CR20","first-page":"1","volume":"6","author":"M Kaminskas","year":"2016","unstructured":"Kaminskas, M., Bridge, D., Foping, F., Roche, D.: Product-seeded and basket-seeded recommendations for small-scale retailers. J. Data Semant. 6, 1\u201312 (2016). https:\/\/link.springer.com\/article\/10.1007\/s13740-016-0058-3","journal-title":"J. Data Semant."},{"issue":"2","key":"20_CR21","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/nbt1284","volume":"25","author":"MJ Keiser","year":"2007","unstructured":"Keiser, M.J., Roth, B.L., Armbruster, B.N., Ernsberger, P., Irwin, J.J., Shoichet, B.K.: Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25(2), 197\u2013206 (2007)","journal-title":"Nat. Biotechnol."},{"issue":"21","key":"20_CR22","doi-asserted-by":"publisher","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","volume":"27","author":"T van Laarhoven","year":"2011","unstructured":"van Laarhoven, T., Nabuurs, S.B., Marchiori, E.: Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21), 3036\u20133043 (2011)","journal-title":"Bioinformatics"},{"issue":"2","key":"20_CR23","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1093\/bioinformatics\/bts670","volume":"29","author":"JP Mei","year":"2013","unstructured":"Mei, J.P., Kwoh, C.K., Yang, P., Li, X.L., Zheng, J.: Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics 29(2), 238\u2013245 (2013)","journal-title":"Bioinformatics"},{"issue":"2","key":"20_CR24","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/bib\/bbu010","volume":"16","author":"T Pahikkala","year":"2015","unstructured":"Pahikkala, T., Airola, A., Pietil\u00e4, S., Shakyawar, S., Szwajda, A., Tang, J., Aittokallio, T.: Toward more realistic drug-target interaction predictions. Briefings Bioinform. 16(2), 325\u2013337 (2015)","journal-title":"Briefings Bioinform."},{"issue":"6","key":"20_CR25","doi-asserted-by":"publisher","first-page":"e63730","DOI":"10.1371\/journal.pone.0063730","volume":"8","author":"S P\u00e9rot","year":"2013","unstructured":"P\u00e9rot, S., Regad, L., Reyn\u00e8s, C., Sp\u00e9randio, O., Miteva, M.A., Villoutreix, B.O., Camproux, A.C.: Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction. PloS One 8(6), e63730 (2013)","journal-title":"PloS One"},{"key":"20_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-319-04298-5_40","volume-title":"SOFSEM 2014: Theory and Practice of Computer Science","author":"L Peska","year":"2014","unstructured":"Peska, L., Vojtas, P.: Recommending for disloyal customers with low consumption rate. In: Geffert, V., Preneel, B., Rovan, B., \u0160tuller, J., Tjoa, A.M. (eds.) SOFSEM 2014. LNCS, vol. 8327, pp. 455\u2013465. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-04298-5_40"},{"doi-asserted-by":"crossref","unstructured":"Pil\u00e1szy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: 3rd ACM Conference on Recommender Systems, pp. 93\u2013100 (2009)","key":"20_CR27","DOI":"10.1145\/1639714.1639731"},{"issue":"2","key":"20_CR28","first-page":"119","volume":"1","author":"M Plantevit","year":"2009","unstructured":"Plantevit, M., Charnois, T., Klema, J., Rigotti, C., Cr\u00e9milleux, B.: Combining sequence and itemset mining to discover named entities in biomedical texts: a new type of pattern. Int. J. Data Min. Model. Manag. 1(2), 119\u2013148 (2009)","journal-title":"Int. J. Data Min. Model. Manag."},{"key":"20_CR29","first-page":"2487","volume":"11","author":"M Radovanovi\u0107","year":"2010","unstructured":"Radovanovi\u0107, M., Nanopoulos, A., Ivanovi\u0107, M.: Hubs in space: popular nearest neighbors in high-dimensional data. J. Mach. Learn. Res. 11, 2487\u20132531 (2010)","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"S\u00f6nstr\u00f6d, C., Johansson, U., Norinder, U., Bostr\u00f6m, H.: Comprehensible models for predicting molecular interaction with heart-regulating genes. In: 7th IEEE International Conference on Machine Learning and Applications, pp. 559\u2013564 (2008)","key":"20_CR30","DOI":"10.1109\/ICMLA.2008.130"},{"doi-asserted-by":"crossref","unstructured":"Stensbo-Smidt, K., Igel, C., Zirm, A., Pedersen, K.S.: Nearest neighbour regression outperforms model-based prediction of specific star formation rate. In: IEEE International Conference on Big Data, pp. 141\u2013144 (2013)","key":"20_CR31","DOI":"10.1109\/BigData.2013.6691746"},{"issue":"10","key":"20_CR32","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1093\/bioinformatics\/btw003","volume":"32","author":"M Stra\u017ear","year":"2016","unstructured":"Stra\u017ear, M., \u017ditnik, M., Zupan, B., Ule, J., Curk, T.: Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins. Bioinformatics 32(10), 1527\u20131535 (2016)","journal-title":"Bioinformatics"},{"key":"20_CR33","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-662-45620-0_11","volume-title":"Feature Selection for Data and Pattern Recognition","author":"N Toma\u0161ev","year":"2015","unstructured":"Toma\u0161ev, N., Buza, K., Marussy, K., Kis, P.B.: Hubness-aware classification, instance selection and feature construction: survey and extensions to time-series. In: Sta\u0144czyk, U., Jain, L.C. (eds.) Feature Selection for Data and Pattern Recognition. SCI, vol. 584, pp. 231\u2013262. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-45620-0_11"},{"doi-asserted-by":"crossref","unstructured":"Ullrich, K., Kamp, M., G\u00e4rtner, T., Vogt, M., Wrobel, S.: Ligand-based virtual screening with co-regularised support vector regression. In: 16th IEEE International Conference on Data Mining Workshops, pp. 261\u2013268 (2016)","key":"20_CR34","DOI":"10.1109\/ICDMW.2016.0044"},{"key":"20_CR35","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-319-46307-0_30","volume-title":"Discovery Science","author":"K Ullrich","year":"2016","unstructured":"Ullrich, K., Mack, J., Welke, P.: Ligand affinity prediction with multi-pattern kernels. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 474\u2013489. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46307-0_30"},{"doi-asserted-by":"crossref","unstructured":"van Laarhoven, T., Marchiori, E.: Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PloS One 8(6), e66952 (2013)","key":"20_CR36","DOI":"10.1371\/journal.pone.0066952"},{"issue":"13","key":"20_CR37","doi-asserted-by":"publisher","first-page":"i126","DOI":"10.1093\/bioinformatics\/btt234","volume":"29","author":"Y Wang","year":"2013","unstructured":"Wang, Y., Zeng, J.: Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics 29(13), i126\u2013i134 (2013)","journal-title":"Bioinformatics"},{"issue":"Suppl 2","key":"20_CR38","doi-asserted-by":"publisher","first-page":"S6","DOI":"10.1186\/1752-0509-4-S2-S6","volume":"4","author":"Z Xia","year":"2010","unstructured":"Xia, Z., Wu, L.Y., Zhou, X., Wong, S.T.: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol. 4(Suppl 2), S6 (2010)","journal-title":"BMC Syst. Biol."},{"issue":"13","key":"20_CR39","doi-asserted-by":"publisher","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","volume":"24","author":"Y Yamanishi","year":"2008","unstructured":"Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232\u2013i240 (2008)","journal-title":"Bioinformatics"},{"key":"20_CR40","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/978-3-642-40994-3_37","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"P Zhang","year":"2013","unstructured":"Zhang, P., Agarwal, P., Obradovic, Z.: Computational drug repositioning by ranking and integrating multiple data sources. In: Blockeel, H., Kersting, K., Nijssen, S., \u017delezn\u00fd, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 579\u2013594. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40994-3_37"},{"doi-asserted-by":"crossref","unstructured":"Zheng, X., Ding, H., Mamitsuka, H., Zhu, S.: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1025\u20131033 (2013)","key":"20_CR41","DOI":"10.1145\/2487575.2487670"},{"doi-asserted-by":"crossref","unstructured":"Zhu, S., Okuno, Y., Tsujimoto, G., Mamitsuka, H.: A probabilistic model for mining implicit chemical compound-gene relations from literature. Bioinformatics 21(Suppl. 2), ii245\u2013ii251 (2005)","key":"20_CR42","DOI":"10.1093\/bioinformatics\/bti1141"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-71246-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T01:10:47Z","timestamp":1672276247000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-71246-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319712451","9783319712468"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-71246-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"30 December 2017","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":"Skopje","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macedonia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2017","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":"ecml2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2017.ijs.si\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}