{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T14:50:21Z","timestamp":1768143021267,"version":"3.49.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"8-9","license":[{"start":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T00:00:00Z","timestamp":1558051200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T00:00:00Z","timestamp":1558051200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Singapore Ministry of Education Academic Research Fund Tier 1","award":["R-253-000-139-114"],"award-info":[{"award-number":["R-253-000-139-114"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s10994-019-05801-6","type":"journal-article","created":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T21:43:09Z","timestamp":1558129389000},"page":"1395-1420","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep collective matrix factorization for augmented multi-view learning"],"prefix":"10.1007","volume":"108","author":[{"given":"Ragunathan","family":"Mariappan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6748-6864","authenticated-orcid":false,"given":"Vaibhav","family":"Rajan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,17]]},"reference":[{"key":"5801_CR1","unstructured":"Andrew, G., Arora, R., Bilmes, J., & Livescu, K. (2013). Deep canonical correlation analysis. In Proceedings of the 30th international conference on machine learning, pp. 1247\u20131255."},{"issue":"8","key":"5801_CR2","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798\u20131828.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"5801_CR3","unstructured":"Bergstra, J. S., Bardenet, R., Bengio, Y., & K\u00e9gl, B. (2011). Algorithms for hyper-parameter optimization. In Proceedings of the 24th international conference on neural information processing systems, pp. 2546\u20132554."},{"key":"5801_CR4","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281\u2013305.","journal-title":"Journal of Machine Learning Research"},{"key":"5801_CR5","unstructured":"Bonilla, E. V., Chai, K. M., & Williams, C. (2007). Multi-task Gaussian process prediction. In Proceedings of the 20th international conference on neural information processing systems, pp. 153\u2013160."},{"key":"5801_CR6","unstructured":"Bouchard, G., Yin, D., & Guo, S. (2013). Convex collective matrix factorization. In Proceedings of the sixteenth international conference on artificial intelligence and statistics, pp. 144\u2013152."},{"issue":"7","key":"5801_CR7","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1038\/nrg2364","volume":"9","author":"M Boutros","year":"2008","unstructured":"Boutros, M., & Ahringer, J. (2008). The art and design of genetic screens: RNA interference. Nature Reviews Genetics, 9(7), 554.","journal-title":"Nature Reviews Genetics"},{"key":"5801_CR8","doi-asserted-by":"crossref","unstructured":"Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C. C., & Huang, T. S. (2015). Heterogeneous network embedding via deep architectures. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 119\u2013128. ACM.","DOI":"10.1145\/2783258.2783296"},{"key":"5801_CR9","unstructured":"Chen, M., Xu, Z., Weinberger, K., & Sha, F. (2012). Marginalized denoising autoencoders for domain adaptation. In Proceedings of the 29th international conference on machine learning, pp. 1627\u20131634."},{"issue":"4","key":"5801_CR10","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1080\/00401706.2000.10485733","volume":"42","author":"TC Coburn","year":"2000","unstructured":"Coburn, T. C. (2000). Geostatistics for natural resources evaluation. Technometrics, 42(4), 437\u2013438.","journal-title":"Technometrics"},{"issue":"5","key":"5801_CR11","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2019","unstructured":"Cui, P., Wang, X., Pei, J., & Zhu, W. (2019). A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering, 31(5), 833\u2013852.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"5801_CR12","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1\u201330.","journal-title":"Journal of Machine Learning Research"},{"key":"5801_CR13","doi-asserted-by":"crossref","unstructured":"Dong, X., Yu, L., Wu, Z., Sun, Y., Yuan, L., & Zhang, F. (2017). A hybrid collaborative filtering model with deep structure for recommender systems. In Proceedings of the thirty-first AAAI conference on artificial intelligence, pp. 1309\u20131315.","DOI":"10.1609\/aaai.v31i1.10747"},{"issue":"9","key":"5801_CR14","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1038\/nrg2178","volume":"8","author":"TM Frayling","year":"2007","unstructured":"Frayling, T. M. (2007). Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nature Reviews Genetics, 8(9), 657.","journal-title":"Nature Reviews Genetics"},{"key":"5801_CR15","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT Press."},{"key":"5801_CR16","doi-asserted-by":"crossref","unstructured":"Guo, X., Gao, L., Liu, X., & Yin, J. (2017). Improved deep embedded clustering with local structure preservation. In Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp. 1753\u20131759.","DOI":"10.24963\/ijcai.2017\/243"},{"key":"5801_CR17","doi-asserted-by":"crossref","unstructured":"Han, X., Shi, C., Wang, S., Philip, S. Y., & Song, L. (2018). Aspect-level deep collaborative filtering via heterogeneous information networks. In Proceedings of the twenty-seventh international joint conference on artificial intelligence, pp. 3393\u20133399.","DOI":"10.24963\/ijcai.2018\/471"},{"issue":"12","key":"5801_CR18","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1162\/0899766042321814","volume":"16","author":"DR Hardoon","year":"2004","unstructured":"Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004). Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16(12), 2639\u20132664.","journal-title":"Neural Computation"},{"issue":"5786","key":"5801_CR19","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504\u2013507.","journal-title":"Science"},{"issue":"3\/4","key":"5801_CR20","doi-asserted-by":"publisher","first-page":"321","DOI":"10.2307\/2333955","volume":"28","author":"H Hotelling","year":"1936","unstructured":"Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3\/4), 321\u2013377.","journal-title":"Biometrika"},{"issue":"9","key":"5801_CR21","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1109\/TPAMI.2012.271","volume":"35","author":"Y Hu","year":"2013","unstructured":"Hu, Y., Zhang, D., Ye, J., Li, X., & He, X. (2013). Fast and accurate matrix completion via truncated nuclear norm regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(9), 2117\u20132130.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"5801_CR22","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1023\/A:1012771025575","volume":"21","author":"DR Jones","year":"2001","unstructured":"Jones, D. R. (2001). A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21(4), 345\u2013383.","journal-title":"Journal of Global Optimization"},{"key":"5801_CR23","unstructured":"Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. In International conference on learning representations."},{"key":"5801_CR24","unstructured":"Klami, A., Bouchard, G., & Tripathi, A. (2014). Group-sparse embeddings in collective matrix factorization. In International conference on learning representations."},{"issue":"1","key":"5801_CR25","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TEVC.2005.851274","volume":"10","author":"J Knowles","year":"2006","unstructured":"Knowles, J. (2006). ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10(1), 50\u201366.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"12","key":"5801_CR26","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1089\/omi.2015.0151","volume":"19","author":"E Kolker","year":"2015","unstructured":"Kolker, E., et al. (2015). Finding text-supported gene-to-disease co-appearances with MOPED-Digger. Omics: A Journal of Integrative Biology, 19(12), 754\u2013756.","journal-title":"Omics: A Journal of Integrative Biology"},{"issue":"8","key":"5801_CR27","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30\u201337.","journal-title":"Computer"},{"key":"5801_CR28","unstructured":"Lan, C., Wang, J., & Huan, J. (2016). Towards a theoretical understanding of negative transfer in collective matrix factorization. In Proceedings of the thirty-second conference on uncertainty in artificial intelligence, pp. 367\u2013376."},{"issue":"7","key":"5801_CR29","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1101\/gr.118992.110","volume":"21","author":"I Lee","year":"2011","unstructured":"Lee, I., Blom, U. M., Wang, P. I., Shim, J. E., & Marcotte, E. M. (2011). Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Research, 21(7), 1109\u20131121.","journal-title":"Genome Research"},{"key":"5801_CR30","doi-asserted-by":"crossref","unstructured":"Li, X., & She, J. (2017). Collaborative variational autoencoder for recommender systems. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 305\u2013314.","DOI":"10.1145\/3097983.3098077"},{"key":"5801_CR31","doi-asserted-by":"crossref","unstructured":"Li, S., Kawale, J., & Fu, Y. (2015). Deep collaborative filtering via marginalized denoising auto-encoder. In Proceedings of the 24th ACM international conference on information and knowledge management, pp. 811\u2013820.","DOI":"10.1145\/2806416.2806527"},{"key":"5801_CR32","unstructured":"Liu, J., Wang, D., & Ding, Y. (2017). PHD: A probabilistic model of hybrid deep collaborative filtering for recommender systems. In Proceedings of the ninth Asian conference on machine learning, pp. 224\u2013239."},{"issue":"8","key":"5801_CR33","doi-asserted-by":"publisher","first-page":"71171","DOI":"10.1371\/journal.pone.0071171","volume":"8","author":"S Loguercio","year":"2013","unstructured":"Loguercio, S., Good, B. M., & Su, A. I. (2013). Dizeez: An online game for human gene-disease annotation. PLoS ONE, 8(8), 71171.","journal-title":"PLoS ONE"},{"issue":"12","key":"5801_CR34","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1093\/bioinformatics\/btu269","volume":"30","author":"N Natarajan","year":"2014","unstructured":"Natarajan, N., & Dhillon, I. S. (2014). Inductive matrix completion for predicting gene-disease associations. Bioinformatics, 30(12), 60\u201368.","journal-title":"Bioinformatics"},{"key":"5801_CR35","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th international conference on machine learning, pp. 689\u2013696."},{"key":"5801_CR36","doi-asserted-by":"crossref","unstructured":"Opap, K., & Mulder, N. (2017). Recent advances in predicting gene-disease associations. F1000Research 6.","DOI":"10.12688\/f1000research.10788.1"},{"issue":"3","key":"5801_CR37","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1023\/A:1007369909943","volume":"27","author":"M Pazzani","year":"1997","unstructured":"Pazzani, M., & Billsus, D. (1997). Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27(3), 313\u2013331.","journal-title":"Machine Learning"},{"issue":"5","key":"5801_CR38","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1002\/gepi.20580","volume":"35","author":"TH Pers","year":"2011","unstructured":"Pers, T. H., et al. (2011). Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes. Genetic Epidemiology, 35(5), 318\u2013332.","journal-title":"Genetic Epidemiology"},{"issue":"D1","key":"5801_CR39","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1093\/nar\/gkw943","volume":"45","author":"J Pi\u00f1ero","year":"2016","unstructured":"Pi\u00f1ero, J., et al. (2016). DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Research, 45(D1), 833\u2013839.","journal-title":"Nucleic Acids Research"},{"issue":"5","key":"5801_CR40","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1111\/j.1742-4658.2012.08471.x","volume":"279","author":"RM Piro","year":"2012","unstructured":"Piro, R. M., & Di Cunto, F. (2012). Computational approaches to disease-gene prediction: Rationale, classification and successes. The FEBS Journal, 279(5), 678\u2013696.","journal-title":"The FEBS Journal"},{"issue":"2","key":"5801_CR41","first-page":"217","volume":"81","author":"PL Schuyler","year":"1993","unstructured":"Schuyler, P. L., Hole, W. T., Tuttle, M. S., & Sherertz, D. D. (1993). The UMLS metathesaurus: Representing different views of biomedical concepts. Bulletin of the Medical Library Association, 81(2), 217.","journal-title":"Bulletin of the Medical Library Association"},{"issue":"1","key":"5801_CR42","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s13721-017-0154-9","volume":"6","author":"E Seyyedrazzagi","year":"2017","unstructured":"Seyyedrazzagi, E., & Navimipour, N. J. (2017). Disease genes prioritizing mechanisms: A comprehensive and systematic literature review. Network Modeling Analysis in Health Informatics and Bioinformatics, 6(1), 13.","journal-title":"Network Modeling Analysis in Health Informatics and Bioinformatics"},{"issue":"2","key":"5801_CR43","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","volume":"31","author":"C Shi","year":"2019","unstructured":"Shi, C., Hu, B., Zhao, W. X., & Philip, S. Y. (2019). Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2), 357\u2013370.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"5801_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-56212-4","volume-title":"Heterogeneous information network analysis and applications","author":"C Shi","year":"2017","unstructured":"Shi, C., & Philip, S. Y. (2017). Heterogeneous information network analysis and applications. Berlin: Springer."},{"key":"5801_CR45","doi-asserted-by":"crossref","unstructured":"Singh, A. P., & Gordon, G. J. (2008). Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 650\u2013658.","DOI":"10.1145\/1401890.1401969"},{"issue":"5","key":"5801_CR46","doi-asserted-by":"publisher","first-page":"58977","DOI":"10.1371\/journal.pone.0058977","volume":"8","author":"UM Singh-Blom","year":"2013","unstructured":"Singh-Blom, U. M., et al. (2013). Prediction and validation of gene-disease associations using methods inspired by social network analyses. PLoS ONE, 8(5), 58977.","journal-title":"PLoS ONE"},{"key":"5801_CR47","unstructured":"Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. In Proceedings of the 25th international conference on neural information processing systems, pp. 2951\u20132959."},{"key":"5801_CR48","doi-asserted-by":"crossref","unstructured":"Srebro, N., & Shraibman, A. (2005). Rank, trace-norm and max-norm. In International conference on computational learning theory, pp. 545\u2013560.","DOI":"10.1007\/11503415_37"},{"key":"5801_CR49","unstructured":"Swersky, K., Snoek, J., & Adams, R. P. (2013). Multi-task Bayesian optimization. In Proceedings of the 26th international conference on neural information processing systems, pp. 2004\u20132012."},{"key":"5801_CR50","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., et al. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371\u20133408.","journal-title":"Journal of Machine Learning Research"},{"key":"5801_CR51","unstructured":"Wang, W., Arora, R., Livescu, K., & Bilmes, J. (2015). On Deep Multi-view Representation Learning. In Proceedings of the 32nd international conference on machine learning, pp. 1083\u20131092."},{"key":"5801_CR52","doi-asserted-by":"crossref","unstructured":"Wang, Q., Sun, M., Zhan, L., Thompson, P., Ji, S., & Zhou, J. (2017). Multi-modality disease modeling via collective deep matrix factorization. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1155\u20131164.","DOI":"10.1145\/3097983.3098164"},{"key":"5801_CR53","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, N., & Yeung, D.-Y. (2015). Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1235\u20131244.","DOI":"10.1145\/2783258.2783273"},{"issue":"10","key":"5801_CR54","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein, J. N., et al. (2013). The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genetics, 45(10), 1113\u20131120.","journal-title":"Nature Genetics"},{"issue":"3","key":"5801_CR55","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1109\/TCBB.2016.2520947","volume":"14","author":"X Zeng","year":"2017","unstructured":"Zeng, X., Liao, Y., Liu, Y., & Zou, Q. (2017). Prediction and validation of disease genes using HeteSim scores. IEEE\/ACM Transactions on Computational Biology and Bioinformatics, 14(3), 687\u2013695.","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"issue":"18","key":"5801_CR56","doi-asserted-by":"publisher","first-page":"2831","DOI":"10.1093\/bioinformatics\/btw358","volume":"32","author":"H Zhou","year":"2016","unstructured":"Zhou, H., & Skolnick, J. (2016). A knowledge-based approach for predicting gene-disease associations. Bioinformatics, 32(18), 2831\u20132838.","journal-title":"Bioinformatics"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-019-05801-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-019-05801-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-019-05801-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T06:16:08Z","timestamp":1663481768000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-019-05801-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,17]]},"references-count":56,"journal-issue":{"issue":"8-9","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["5801"],"URL":"https:\/\/doi.org\/10.1007\/s10994-019-05801-6","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,17]]},"assertion":[{"value":"26 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}