{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:44:46Z","timestamp":1783410286679,"version":"3.54.6"},"reference-count":127,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2018,6,28]],"date-time":"2018-06-28T00:00:00Z","timestamp":1530144000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"FDCT Macau SAR Government","award":["FDCT\/126\/2014\/A3"],"award-info":[{"award-number":["FDCT\/126\/2014\/A3"]}]},{"DOI":"10.13039\/501100004733","name":"Universidade de Macau","doi-asserted-by":"publisher","award":["MYRG2016-00069"],"award-info":[{"award-number":["MYRG2016-00069"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1007\/s10916-018-1003-9","type":"journal-article","created":{"date-parts":[[2018,6,28]],"date-time":"2018-06-28T08:02:22Z","timestamp":1530172942000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":233,"title":["A Survey of Data Mining and Deep Learning in Bioinformatics"],"prefix":"10.1007","volume":"42","author":[{"given":"Kun","family":"Lan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan-tong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Fong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lian-sheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelvin K. L.","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilanjan","family":"Dey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,6,28]]},"reference":[{"issue":"6","key":"1003_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MIS.2005.108","volume":"20","author":"J Li","year":"2005","unstructured":"Li, J., Wong, L., and Yang, Q., Guest editors' introduction: Data Mining in Bioinformatics. IEEE Intell. Syst. 20(6):16\u201318, 2005.","journal-title":"IEEE Intell. Syst."},{"issue":"4","key":"1003_CR2","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1007\/s10916-011-9710-5","volume":"36","author":"I Yoo","year":"2012","unstructured":"Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J.-F., and Hua, L., Data mining in healthcare and biomedicine: a survey of the literature. J. Med. Syst. 36(4):2431\u20132448, 2012.","journal-title":"J. Med. Syst."},{"key":"1003_CR3","doi-asserted-by":"crossref","unstructured":"Kharya, S., Using data mining techniques for diagnosis and prognosis of cancer disease. arXiv preprint arXiv:12051923, 2012.","DOI":"10.5121\/ijcseit.2012.2206"},{"key":"1003_CR4","doi-asserted-by":"crossref","unstructured":"Santosh, K., and Antani, S., Automated chest X-ray screening: Can lung region symmetry help detect pulmonary abnormalities? IEEE Transactions on Medical Imaging, 2017.","DOI":"10.1109\/TMI.2017.2775636"},{"key":"1003_CR5","unstructured":"Zohora, F. T., Antani, S., and Santosh, K., Circle-like foreign element detection in chest x-rays using normalized cross-correlation and unsupervised clustering. In: Medical Imaging 2018: Image Processing. International Society for Optics and Photonics, p 105741V, 2018."},{"issue":"2","key":"1003_CR6","doi-asserted-by":"publisher","first-page":"36","DOI":"10.4018\/IJCVIP.2017040103","volume":"7","author":"FT Zohora","year":"2017","unstructured":"Zohora, F. T., and Santosh, K., Foreign Circular Element Detection in Chest X-Rays for Effective Automated Pulmonary Abnormality Screening. International Journal of Computer Vision and Image Processing (IJCVIP). 7(2):36\u201349, 2017.","journal-title":"International Journal of Computer Vision and Image Processing (IJCVIP)."},{"issue":"9","key":"1003_CR7","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1007\/s11548-016-1359-6","volume":"11","author":"K Santosh","year":"2016","unstructured":"Santosh, K., Vajda, S., Antani, S., and Thoma, G. R., Edge map analysis in chest X-rays for automatic pulmonary abnormality screening. Int. J. Comput. Assist. Radiol. Surg. 11(9):1637\u20131646, 2016.","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"1003_CR8","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s11548-015-1242-x","volume":"11","author":"A Karargyris","year":"2016","unstructured":"Karargyris, A., Siegelman, J., Tzortzis, D., Jaeger, S., Candemir, S., Xue, Z., Santosh, K., Vajda, S., Antani, S., and Folio, L., Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays. Int. J. Comput. Assist. Radiol. Surg. 11(1):99\u2013106, 2016.","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"1003_CR9","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s10916-017-0851-z","volume":"42","author":"S Kalsi","year":"2018","unstructured":"Kalsi, S., Kaur, H., and Chang, V., DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation. J. Med. Syst. 42(1):17, 2018.","journal-title":"J. Med. Syst."},{"issue":"5","key":"1003_CR10","doi-asserted-by":"publisher","first-page":"2841","DOI":"10.1007\/s10916-011-9762-6","volume":"36","author":"S-L Hsieh","year":"2012","unstructured":"Hsieh, S.-L., Hsieh, S.-H., Cheng, P.-H., Chen, C.-H., Hsu, K.-P., Lee, I.-S., Wang, Z., and Lai, F., Design ensemble machine learning model for breast cancer diagnosis. J. Med. Syst. 36(5):2841\u20132847, 2012.","journal-title":"J. Med. Syst."},{"issue":"12","key":"1003_CR11","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s10916-017-0853-x","volume":"41","author":"S Somasundaram","year":"2017","unstructured":"Somasundaram, S., Alli, P., and Machine Learning, A., Ensemble Classifier for Early Prediction of Diabetic Retinopathy. J. Med. Syst. 41(12):201, 2017.","journal-title":"J. Med. Syst."},{"issue":"4","key":"1003_CR12","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10916-017-0715-6","volume":"41","author":"HO Alanazi","year":"2017","unstructured":"Alanazi, H. O., Abdullah, A. H., and Qureshi, K. N., A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J. Med. Syst. 41(4):69, 2017.","journal-title":"J. Med. Syst."},{"key":"1003_CR13","unstructured":"Han, J., How can data mining help bio-data analysis? In: Proceedings of the 2nd International Conference on Data Mining in Bioinformatics. Springer-Verlag, pp 1\u20132, 2002."},{"issue":"4","key":"1003_CR14","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1038\/ng1201-365","volume":"29","author":"A Brazma","year":"2001","unstructured":"Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C. A., and Causton, H. C., Minimum information about a microarray experiment (MIAME)\u2014toward standards for microarray data. Nat. Genet. 29(4):365\u2013371, 2001.","journal-title":"Nat. Genet."},{"key":"1003_CR15","unstructured":"Antonie, M.-L., Zaiane, O. R., and Coman, A. Application of data mining techniques for medical image classification. In: Proceedings of the Second International Conference on Multimedia Data Mining. Springer-Verlag, pp. 94\u2013101, 2001."},{"key":"1003_CR16","doi-asserted-by":"crossref","unstructured":"Dasu, T., Johnson, T., Muthukrishnan, S., and Shkapenyuk, V., Mining database structure; or, how to build a data quality browser. In: Proceedings of the 2002 ACM SIGMOD international conference on Management of data. ACM, pp 240\u2013251, 2002.","DOI":"10.1145\/564716.564719"},{"key":"1003_CR17","unstructured":"Raman, V., and Hellerstein, J. M., Potter's wheel: An interactive data cleaning system. In: VLDB, pp 381\u2013390, 2001."},{"key":"1003_CR18","first-page":"237","volume":"18","author":"B Becker","year":"2001","unstructured":"Becker, B., Kohavi, R., and Sommerfield, D., Visualizing the simple Bayesian classifier. Information Visualization in Data Mining and Knowledge Discovery. 18:237\u2013249, 2001.","journal-title":"Information Visualization in Data Mining and Knowledge Discovery."},{"key":"1003_CR19","unstructured":"Zhang, J., Hsu, W., and Lee, M., FASTCiD: FAST clustering in dynamic spatial databases. Submitted for publication, 2002."},{"key":"1003_CR20","doi-asserted-by":"crossref","unstructured":"Xu, X., J\u00e4ger, J., and Kriegel, H.-P., A fast parallel clustering algorithm for large spatial databases. In: High Performance Data Mining. Springer, pp 263\u2013290, 1999.","DOI":"10.1007\/0-306-47011-X_3"},{"key":"1003_CR21","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han, J., Pei, J., and Kamber, M., Data mining: concepts and techniques. New York: Elsevier, 2011."},{"key":"1003_CR22","doi-asserted-by":"crossref","unstructured":"Daubechies, I., Ten lectures on wavelets. SIAM, 1992.","DOI":"10.1137\/1.9781611970104"},{"key":"1003_CR23","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","volume":"19","author":"A Mackiewicz","year":"1993","unstructured":"Mackiewicz, A., and Ratajczak, W., Principal components analysis (PCA). Comput. Geosci. 19:303\u2013342, 1993.","journal-title":"Comput. Geosci."},{"key":"1003_CR24","first-page":"30602","volume-title":"Principal components analysis (PCA). Department of Geology","author":"SM Holland","year":"2008","unstructured":"Holland, S. M., Principal components analysis (PCA). Department of Geology. Athens, GA: University of Georgia, 2008, 30602\u201332501."},{"issue":"1","key":"1003_CR25","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/0169-7439(95)00076-3","volume":"30","author":"W Ku","year":"1995","unstructured":"Ku, W., Storer, R. H., and Georgakis, C., Disturbance detection and isolation by dynamic principal component analysis. Chemom. Intell. Lab. Syst. 30(1):179\u2013196, 1995.","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"4","key":"1003_CR26","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TCOM.1976.1093309","volume":"24","author":"H Andrews","year":"1976","unstructured":"Andrews, H., and Patterson, C., Singular value decomposition (SVD) image coding. IEEE Trans. Commun. 24(4):425\u2013432, 1976.","journal-title":"IEEE Trans. Commun."},{"issue":"4","key":"1003_CR27","first-page":"13","volume":"5","author":"C Shearer","year":"2000","unstructured":"Shearer, C., The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing 5(4):13\u201322, 2000.","journal-title":"Journal of Data Warehousing"},{"issue":"1","key":"1003_CR28","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1186\/1471-2164-7-278","volume":"7","author":"AM Glas","year":"2006","unstructured":"Glas, A. M., Floore, A., Delahaye, L. J., Witteveen, A. T., Pover, R. C., Bakx, N., Lahti-Domenici, J. S., Bruinsma, T. J., Warmoes, M. O., and Bernards, R., Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 7(1):278, 2006.","journal-title":"BMC Genomics"},{"key":"1003_CR29","doi-asserted-by":"crossref","unstructured":"Yoshida, H., Kawaguchi, A., and Tsuruya, K., Radial basis function-sparse partial least squares for application to brain imaging data. Computational and Mathematical Methods in Medicine 2013, 2013.","DOI":"10.1155\/2013\/591032"},{"issue":"10","key":"1003_CR30","doi-asserted-by":"publisher","first-page":"8852","DOI":"10.1016\/j.eswa.2012.02.004","volume":"39","author":"C-H Jen","year":"2012","unstructured":"Jen, C.-H., Wang, C.-C., Jiang, B. C., Chu, Y.-H., and Chen, M.-S., Application of classification techniques on development an early-warning system for chronic illnesses. Expert Syst. Appl. 39(10):8852\u20138858, 2012.","journal-title":"Expert Syst. Appl."},{"key":"1003_CR31","first-page":"311","volume":"4","author":"T Bailey","year":"1978","unstructured":"Bailey, T., and Jain, A., A note on distance-weighted $ k $-nearest neighbor rules. IEEE Trans Syst Man Cybern 4:311\u2013313, 1978.","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"1003_CR32","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1109\/TSMC.1985.6313426","volume":"4","author":"JM Keller","year":"1985","unstructured":"Keller, J. M., Gray, M. R., and Givens, J. A., A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 4:580\u2013585, 1985.","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"5","key":"1003_CR33","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1007\/s10916-011-9815-x","volume":"36","author":"D-Y Liu","year":"2012","unstructured":"Liu, D.-Y., Chen, H.-L., Yang, B., Lv, X.-E., Li, L.-N., and Liu, J., Design of an enhanced fuzzy k-nearest neighbor classifier based computer aided diagnostic system for thyroid disease. J. Med. Syst. 36(5):3243\u20133254, 2012.","journal-title":"J. Med. Syst."},{"key":"1003_CR34","doi-asserted-by":"crossref","unstructured":"Syaliman, K., and Nababan, E., Sitompul O Improving the accuracy of k-nearest neighbor using local mean based and distance weight. In: Journal of Physics: Conference Series. vol 1. IOP Publishing, p 012047, 2018.","DOI":"10.1088\/1742-6596\/978\/1\/012047"},{"key":"1003_CR35","doi-asserted-by":"crossref","unstructured":"Spiegelhalter, D. J., Dawid, A. P., Lauritzen, S. L., Cowell, R. G., Bayesian analysis in expert systems. Statistical science: 219\u2013247, 1993.","DOI":"10.1214\/ss\/1177010888"},{"key":"1003_CR36","doi-asserted-by":"crossref","unstructured":"Kononenko, I., Semi-naive Bayesian classifier. In: Machine Learning\u2014EWSL-91. Springer, pp 206\u2013219, 1991.","DOI":"10.1007\/BFb0017015"},{"key":"1003_CR37","doi-asserted-by":"crossref","unstructured":"Langley, P., Induction of recursive Bayesian classifiers. In: Machine Learning: ECML-93. Springer, pp 153\u2013164, 1993.","DOI":"10.1007\/3-540-56602-3_134"},{"key":"1003_CR38","unstructured":"Peng, H., and Long, F. A., Bayesian learning algorithm of discrete variables for automatically mining irregular features of pattern images. In: Proceedings of the Second International Conference on Multimedia Data Mining. Springer-Verlag, pp 87\u201393, 2001."},{"issue":"2","key":"1003_CR39","first-page":"7","volume":"13","author":"SJ Hickey","year":"2013","unstructured":"Hickey, S. J., Naive Bayes classification of public health data with greedy feature selection. Commun. IIMA 13(2):7, 2013.","journal-title":"Commun. IIMA"},{"issue":"6","key":"1003_CR40","doi-asserted-by":"publisher","first-page":"247","DOI":"10.3390\/e19060247","volume":"19","author":"J Abell\u00e1n","year":"2017","unstructured":"Abell\u00e1n, J., and Castellano, J. G., Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy. Entropy 19(6):247, 2017.","journal-title":"Entropy"},{"key":"1003_CR41","doi-asserted-by":"crossref","unstructured":"Estella, F., Delgado-Marquez, B. L., Rojas, P., Valenzuela, O., San Roman, B., and Rojas, I., Advanced system for automously classify brain MRI in neurodegenerative disease. In: Multimedia Computing and Systems (ICMCS), 2012 International Conference on. IEEE, pp 250\u2013255, 2012.","DOI":"10.1109\/ICMCS.2012.6320281"},{"issue":"10","key":"1003_CR42","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","volume":"28","author":"JJ Rodriguez","year":"2006","unstructured":"Rodriguez, J. J., Kuncheva, L. I., and Alonso, C. J., Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10):1619\u20131630, 2006.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1003_CR43","doi-asserted-by":"crossref","unstructured":"Domingos, P., and Hulten, G., Mining high-speed data streams. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 71\u201380, 2000.","DOI":"10.1145\/347090.347107"},{"key":"1003_CR44","doi-asserted-by":"crossref","unstructured":"Hulten, G., Spencer, L., and Domingos, P., Mining time-changing data streams. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 97\u2013106, 2001.","DOI":"10.1145\/502512.502529"},{"key":"1003_CR45","unstructured":"Zhu, B., Jiao, J., Han, Y., Weissman, T., Improving Decision Tree Learning by Optimal Split Scoring Function Estimation, 2017."},{"key":"1003_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1613\/jair.2602","volume":"33","author":"S Esmeir","year":"2008","unstructured":"Esmeir, S., and Markovitch, S., Anytime induction of low-cost, low-error classifiers: a sampling-based approach. J. Artif. Intell. Res. 33:1\u201331, 2008.","journal-title":"J. Artif. Intell. Res."},{"issue":"3","key":"1003_CR47","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s10994-010-5228-1","volume":"82","author":"S Esmeir","year":"2011","unstructured":"Esmeir, S., and Markovitch, S., Anytime learning of anycost classifiers. Mach. Learn. 82(3):445\u2013473, 2011.","journal-title":"Mach. Learn."},{"key":"1003_CR48","doi-asserted-by":"crossref","unstructured":"Boser, B. E., Guyon, I. M., and Vapnik, V. N. A., training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory. ACM, pp 144\u2013152, 1992.","DOI":"10.1145\/130385.130401"},{"key":"1003_CR49","doi-asserted-by":"crossref","unstructured":"Lee, K.-J., Hwang, Y.-S., and Rim, H.-C., Two-phase biomedical NE recognition based on SVMs. In: Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine-Volume 13. Association for Computational Linguistics, pp 33\u201340, 2003.","DOI":"10.3115\/1118958.1118963"},{"issue":"3","key":"1003_CR50","doi-asserted-by":"publisher","first-page":"3634","DOI":"10.1016\/j.eswa.2011.09.054","volume":"39","author":"L Nanni","year":"2012","unstructured":"Nanni, L., Lumini, A., and Brahnam, S., Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39(3):3634\u20133641, 2012.","journal-title":"Expert Syst. Appl."},{"issue":"4\u20132","key":"1003_CR51","first-page":"1589","volume":"7","author":"NNM Hasri","year":"2017","unstructured":"Hasri, N. N. M., Wen, N. H., Howe, C. W., Mohamad, M. S., Deris, S., and Kasim, S., Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification. International Journal on Advanced Science, Engineering and Information. Technology 7(4\u20132):1589\u20131594, 2017.","journal-title":"Technology"},{"key":"1003_CR52","doi-asserted-by":"crossref","unstructured":"Kavitha, K., and Gopinath, A., Gopi M Applying improved svm classifier for leukemia cancer classification using FCBF. In: Advances in Computing, Coemmunications and Informatics (ICACCI), 2017 International Conference on. IEEE, pp 61\u201366, 2017.","DOI":"10.1109\/ICACCI.2017.8125817"},{"issue":"12","key":"1003_CR53","doi-asserted-by":"publisher","first-page":"7648","DOI":"10.1016\/j.eswa.2010.04.078","volume":"37","author":"O Er","year":"2010","unstructured":"Er, O., Yumusak, N., and Temurtas, F., Chest diseases diagnosis using artificial neural networks. Expert Syst. Appl. 37(12):7648\u20137655, 2010.","journal-title":"Expert Syst. Appl."},{"key":"1003_CR54","doi-asserted-by":"crossref","unstructured":"Gunasundari, S., and Baskar S., Application of Artificial Neural Network in identification of lung diseases. In: Nature & Biologically Inspired Computing. NaBIC 2009. World Congress on. IEEE, pp 1441\u20131444, 2009.","DOI":"10.1109\/NABIC.2009.5393702"},{"key":"1003_CR55","unstructured":"Bin, W., and Jing, Z., A novel artificial neural network and an improved particle swarm optimization used in splice site prediction. J Appl Computat Math 3(166), 2014."},{"issue":"18","key":"1003_CR56","doi-asserted-by":"publisher","first-page":"2010","DOI":"10.1093\/bioinformatics\/btn356","volume":"24","author":"D Amaratunga","year":"2008","unstructured":"Amaratunga, D., Cabrera, J., and Lee, Y.-S., Enriched random forests. Bioinformatics 24(18):2010\u20132014, 2008.","journal-title":"Bioinformatics"},{"issue":"1","key":"1003_CR57","doi-asserted-by":"publisher","first-page":"62","DOI":"10.2174\/1874129001307010062","volume":"7","author":"D Yao","year":"2013","unstructured":"Yao, D., Yang, J., and Zhan, X., An improved random forest algorithm for class-imbalanced data classification and its application in PAD risk factors analysis. Open Electr Electron Eng J 7(1):62\u201372, 2013.","journal-title":"Open Electr Electron Eng J"},{"key":"1003_CR58","first-page":"8","volume":"1","author":"F Fabris","year":"2018","unstructured":"Fabris, F., Doherty, A., Palmer, D., de Magalh\u00e3es, J. P., Freitas, A. A., and Wren, J., A new approach for interpreting Random Forest models and its application to the biology of ageing. Bioinformatics 1:8, 2018.","journal-title":"Bioinformatics"},{"key":"1003_CR59","volume-title":"Information mining\u2014Reflections on recent advancements and the road ahead in data, text, and media mining","author":"R Gopal","year":"2011","unstructured":"Gopal, R., Marsden, J. R., and Vanthienen, J., Information mining\u2014Reflections on recent advancements and the road ahead in data, text, and media mining. New York, NY: Elsevier, 2011."},{"key":"1003_CR60","unstructured":"Ding, J., Berleant, D., Nettleton, D., and Wurtele, E., Mining MEDLINE: abstracts, sentences, or phrases. In: Proceedings of the pacific symposium on biocomputing, 2002. pp 326\u2013337, 2002."},{"issue":"14","key":"1003_CR61","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1093\/bioinformatics\/btl170","volume":"22","author":"H-B Shen","year":"2006","unstructured":"Shen, H.-B., and Chou, K.-C., Ensemble classifier for protein fold pattern recognition. Bioinformatics 22(14):1717\u20131722, 2006.","journal-title":"Bioinformatics"},{"issue":"4","key":"1003_CR62","doi-asserted-by":"publisher","first-page":"2465","DOI":"10.1016\/j.eswa.2007.04.015","volume":"34","author":"J-H Eom","year":"2008","unstructured":"Eom, J.-H., Kim, S.-C., and Zhang, B.-T., AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction. Expert Syst. Appl. 34(4):2465\u20132479, 2008.","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"1003_CR63","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain, A. K., Murty, M. N., and Flynn, P. J., Data clustering: a review. ACM computing surveys (CSUR) 31(3):264\u2013323, 1999.","journal-title":"ACM computing surveys (CSUR)"},{"key":"1003_CR64","unstructured":"Zhang, T., Ramakrishnan, R., and Livny, M., BIRCH: an efficient data clustering method for very large databases. In: ACM Sigmod Record. vol 2. ACM, pp 103\u2013114, 1996."},{"issue":"2","key":"1003_CR65","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1093\/molbev\/msh018","volume":"21","author":"D Bryant","year":"2004","unstructured":"Bryant, D., and Moulton, V., Neighbor-net: an agglomerative method for the construction of phylogenetic networks. Mol. Biol. Evol. 21(2):255\u2013265, 2004.","journal-title":"Mol. Biol. Evol."},{"issue":"4","key":"1003_CR66","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1111\/j.1541-0420.2008.00993.x","volume":"64","author":"M Heo","year":"2008","unstructured":"Heo, M., and Leon, A. C., Statistical power and sample size requirements for three level hierarchical cluster randomized trials. Biometrics 64(4):1256\u20131262, 2008.","journal-title":"Biometrics"},{"issue":"4","key":"1003_CR67","doi-asserted-by":"publisher","first-page":"e59795","DOI":"10.1371\/journal.pone.0059795","volume":"8","author":"R Darkins","year":"2013","unstructured":"Darkins, R., Cooke, E. J., Ghahramani, Z., Kirk, P. D., Wild, D. L., and Savage, R. S., Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm. PLoS One 8(4):e59795, 2013.","journal-title":"PLoS One"},{"issue":"2","key":"1003_CR68","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/s10489-014-0573-6","volume":"42","author":"A Elkamel","year":"2015","unstructured":"Elkamel, A., Gzara, M., and Ben-Abdallah, H., A bio-inspired hierarchical clustering algorithm with backtracking strategy. Appl. Intell. 42(2):174\u2013194, 2015.","journal-title":"Appl. Intell."},{"issue":"4","key":"1003_CR69","doi-asserted-by":"publisher","first-page":"77","DOI":"10.4316\/AECE.2017.04010","volume":"17","author":"P Yildirim","year":"2017","unstructured":"Yildirim, P., and Birant, D., K-Linkage: A New Agglomerative Approach for Hierarchical Clustering. Adv Electr Comput Eng 17(4):77\u201388, 2017.","journal-title":"Adv Electr Comput Eng"},{"key":"1003_CR70","doi-asserted-by":"crossref","unstructured":"Chiu, T., Fang, D., Chen, J., Wang, Y., and Jeris, C., A robust and scalable clustering algorithm for mixed type attributes in large database environment. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 263\u2013268, 2001.","DOI":"10.1145\/502512.502549"},{"key":"1003_CR71","unstructured":"Hussain, H. M., Benkrid, K., Seker, H., and Erdogan, A. T., FPGA implementation of K-means algorithm for bioinformatics application: An accelerated approach to clustering Microarray data. In: Adaptive Hardware and Systems (AHS), 2011 NASA\/ESA Conference on. IEEE, pp 248\u2013255, 2011."},{"issue":"17","key":"1003_CR72","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1093\/bioinformatics\/btm320","volume":"23","author":"GC Tseng","year":"2007","unstructured":"Tseng, G. C., Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data. Bioinformatics 23(17):2247\u20132255, 2007.","journal-title":"Bioinformatics"},{"issue":"1","key":"1003_CR73","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12918-017-0420-6","volume":"11","author":"JA Bot\u00eda","year":"2017","unstructured":"Bot\u00eda, J. A., Vandrovcova, J., Forabosco, P., Guelfi, S., D\u2019Sa, K., Hardy, J., Lewis, C. M., Ryten, M., and Weale, M. E., An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks. BMC Syst. Biol. 11(1):47, 2017.","journal-title":"BMC Syst. Biol."},{"issue":"1","key":"1003_CR74","first-page":"100","volume":"20","author":"G Sathiya","year":"2014","unstructured":"Sathiya, G., and Kavitha, P., An efficient enhanced K-means approach with improved initial cluster centers. Middle-East J. Sci. Res. 20(1):100\u2013107, 2014.","journal-title":"Middle-East J. Sci. Res."},{"issue":"8","key":"1003_CR75","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain, A. K., Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8):651\u2013666, 2010.","journal-title":"Pattern Recogn. Lett."},{"key":"1003_CR76","unstructured":"Jiang, D., Pei, J., and Zhang, A., DHC: a density-based hierarchical clustering method for time series gene expression data. In: Bioinformatics and Bioengineering. Proceedings. Third IEEE Symposium on, 2003. IEEE, pp 393\u2013400, 2003."},{"key":"1003_CR77","doi-asserted-by":"crossref","unstructured":"Kailing, K., Kriegel, H.-P., and Kr\u00f6ger, P., Density-connected subspace clustering for high-dimensional data. In: Proceedings of the 2004 SIAM International Conference on Data Mining. SIAM, pp 246\u2013256, 2004.","DOI":"10.1137\/1.9781611972740.23"},{"key":"1003_CR78","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, M., Han, X., and Zheng, K., An improved density-based spatial clustering of application with noise. International Journal of Computers and Applications: 1\u20137, 2018.","DOI":"10.1080\/1206212X.2018.1424103"},{"key":"1003_CR79","unstructured":"G\u00fcnnemann, S., Boden, B., and Seidl, T., DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors. Machine Learning and Knowledge Discovery in Databases:565\u2013580, 2011."},{"issue":"5","key":"1003_CR80","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1021\/acs.jctc.5b01233","volume":"12","author":"F Sittel","year":"2016","unstructured":"Sittel, F., and Stock, G., Robust density-based clustering to identify metastable conformational states of proteins. J. Chem. Theory Comput. 12(5):2426\u20132435, 2016.","journal-title":"J. Chem. Theory Comput."},{"issue":"3","key":"1003_CR81","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1002\/jcc.24664","volume":"38","author":"S Liu","year":"2017","unstructured":"Liu, S., Zhu, L., Sheong, F. K., Wang, W., and Huang, X., Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories. J. Comput. Chem. 38(3):152\u2013160, 2017.","journal-title":"J. Comput. Chem."},{"issue":"suppl_1","key":"1003_CR82","doi-asserted-by":"publisher","first-page":"D369","DOI":"10.1093\/nar\/gkj095","volume":"34","author":"N Maltsev","year":"2006","unstructured":"Maltsev, N., Glass, E., Sulakhe, D., Rodriguez, A., Syed, M. H., Bompada, T., Zhang, Y., and D'souza, M., PUMA2\u2014grid-based high-throughput analysis of genomes and metabolic pathways. Nucleic Acids Res. 34(suppl_1):D369\u2013D372, 2006.","journal-title":"Nucleic Acids Res."},{"issue":"12","key":"1003_CR83","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1093\/bioinformatics\/btl115","volume":"22","author":"F Ortuso","year":"2006","unstructured":"Ortuso, F., Langer, T., and Alcaro, S., GBPM: GRID-based pharmacophore model: concept and application studies to protein\u2013protein recognition. Bioinformatics 22(12):1449\u20131455, 2006.","journal-title":"Bioinformatics"},{"issue":"1","key":"1003_CR84","doi-asserted-by":"publisher","first-page":"S7","DOI":"10.1186\/1471-2105-8-S1-S7","volume":"8","author":"I Porro","year":"2007","unstructured":"Porro, I., Torterolo, L., Corradi, L., Fato, M., Papadimitropoulos, A., Scaglione, S., Schenone, A., and Viti, F., A Grid-based solution for management and analysis of microarrays in distributed experiments. BMC Bioinf 8(1):S7, 2007.","journal-title":"BMC Bioinf"},{"key":"1003_CR85","doi-asserted-by":"publisher","unstructured":"Ren, J., Cai, B., and Hu, C., Clustering over data streams based on grid density and index tree. 6. https:\/\/doi.org\/10.4156\/jcit.vol6.issue1.11 , 2011.","DOI":"10.4156\/jcit.vol6.issue1.11"},{"key":"1003_CR86","unstructured":"Liu, F., Ye, C., and Zhu, E., Accurate Grid-based Clustering Algorithm with Diagonal Grid Searching and Merging. In: IOP Conference Series: Materials Science and Engineering. 1: IOP Publishing, p 012123, 2017."},{"issue":"2","key":"1003_CR87","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1093\/bioinformatics\/btt632","volume":"30","author":"Y Si","year":"2013","unstructured":"Si, Y., Liu, P., Li, P., and Brutnell, T. P., Model-based clustering for RNA-seq data. Bioinformatics 30(2):197\u2013205, 2013.","journal-title":"Bioinformatics"},{"issue":"3","key":"1003_CR88","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/j.cmpb.2013.08.002","volume":"112","author":"JH Abawajy","year":"2013","unstructured":"Abawajy, J. H., Kelarev, A. V., and Chowdhury, M., Multistage approach for clustering and classification of ECG data. Comput. Methods Prog. Biomed. 112(3):720\u2013730, 2013.","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"1","key":"1003_CR89","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/1471-2105-3-36","volume":"3","author":"J Wang","year":"2002","unstructured":"Wang, J., Delabie, J., Aasheim, H. C., Smeland, E., and Myklebost, O., Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinf 3(1):36, 2002. https:\/\/doi.org\/10.1186\/1471-2105-3-36 .","journal-title":"BMC Bioinf"},{"issue":"5786","key":"1003_CR90","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E., and Salakhutdinov, R. R., Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507, 2006.","journal-title":"Science"},{"issue":"7","key":"1003_CR91","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E., Osindero, S., and Teh, Y.-W., A fast learning algorithm for deep belief nets. Neural Comput. 18(7):1527\u20131554, 2006.","journal-title":"Neural Comput."},{"key":"1003_CR92","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H., Greedy layer-wise training of deep networks. In: Advances in neural information processing systems. pp 153\u2013160, 2007.","DOI":"10.7551\/mitpress\/7503.003.0024"},{"issue":"11","key":"1003_CR93","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86(11):2278\u20132324, 1998.","journal-title":"Proc. IEEE"},{"key":"1003_CR94","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y., On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning. pp 1310\u20131318, 2013."},{"issue":"1","key":"1003_CR95","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","volume":"160","author":"DH Hubel","year":"1962","unstructured":"Hubel, D. H., and Wiesel, T. N., Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160(1):106\u2013154, 1962.","journal-title":"J. Physiol."},{"key":"1003_CR96","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiang, L., Hang, R., and Wu, J., Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology. In: Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on. IEEE, pp 999\u20131002, 2014.","DOI":"10.1109\/ISBI.2014.6868041"},{"issue":"10","key":"1003_CR97","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s10916-017-0814-4","volume":"41","author":"W Jia","year":"2017","unstructured":"Jia, W., Yang, M., and Wang, S.-H., Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep Autoencoder. J. Med. Syst. 41(10):165, 2017.","journal-title":"J. Med. Syst."},{"key":"1003_CR98","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A., Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning. ACM, pp 1096\u20131103, 2008.","DOI":"10.1145\/1390156.1390294"},{"key":"1003_CR99","doi-asserted-by":"crossref","unstructured":"Huang, G. B., Lee, H., and Learned-Miller, E., Learning hierarchical representations for face verification with convolutional deep belief networks. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, pp 2518\u20132525. , 2012.","DOI":"10.1109\/CVPR.2012.6247968"},{"key":"1003_CR100","unstructured":"Lee, H., Pham, P., Largman, Y., Ng AY., Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Advances in neural information processing systems, 2009. pp 1096\u20131104, 2009."},{"issue":"7553","key":"1003_CR101","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., and Hinton, G., Deep learning. Nature 521(7553):436\u2013444, 2015.","journal-title":"Nature"},{"issue":"2","key":"1003_CR102","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., and Frasconi, P., Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2):157\u2013166, 1994.","journal-title":"IEEE Trans. Neural Netw."},{"key":"1003_CR103","unstructured":"Gers, F. A., Schmidhuber, J., and Cummins F., Learning to forget: Continual prediction with LSTM."},{"key":"1003_CR104","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio Y., Learnieng phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:14061078"},{"key":"1003_CR105","unstructured":"Fakoor, R., Ladhak, F., Nazi, A., and Huber, M., Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of the International Conference on Machine Learning, 2013."},{"issue":"4","key":"1003_CR106","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TCBB.2014.2377729","volume":"12","author":"M Liang","year":"2015","unstructured":"Liang, M., Li, Z., Chen, T., and Zeng, J., Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE\/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 12(4):928\u2013937, 2015.","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics (TCBB)"},{"issue":"11","key":"1003_CR107","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1109\/TBME.2015.2444389","volume":"62","author":"X Gao","year":"2015","unstructured":"Gao, X., Lin, S., and Wong, T. Y., Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans. Biomed. Eng. 62(11):2693\u20132701, 2015.","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1003_CR108","unstructured":"Liao, S., Gao, Y., Oto, A., and Shen, D., Representation learning: a unified deep learning framework for automatic prostate MR segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013. Springer, pp 254\u2013261, 2013."},{"issue":"19","key":"1003_CR109","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1093\/bioinformatics\/bts475","volume":"28","author":"P Lena Di","year":"2012","unstructured":"Di Lena, P., Nagata, K., and Baldi, P., Deep architectures for protein contact map prediction. Bioinformatics 28(19):2449\u20132457, 2012.","journal-title":"Bioinformatics"},{"issue":"6","key":"1003_CR110","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/TNB.2015.2461219","volume":"14","author":"G Ditzler","year":"2015","unstructured":"Ditzler, G., Polikar, R., and Rosen, G., Multi-layer and recursive neural networks for metagenomic classification. IEEE Trans on Nanobiosci 14(6):608\u2013616, 2015.","journal-title":"IEEE Trans on Nanobiosci"},{"key":"1003_CR111","unstructured":"Majumdar, A., Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder. arXiv preprint arXiv:150306383, 2015."},{"issue":"10","key":"1003_CR112","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1021\/acs.jcim.5b00238","volume":"55","author":"Y Xu","year":"2015","unstructured":"Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., and Lai, L., Deep learning for drug-induced liver injury. J. Chem. Inf. Model. 55(10):2085\u20132093, 2015.","journal-title":"J. Chem. Inf. Model."},{"issue":"6","key":"1003_CR113","doi-asserted-by":"publisher","first-page":"I1","DOI":"10.1186\/1471-2105-15-S6-I1","volume":"15","author":"A Holzinger","year":"2014","unstructured":"Holzinger, A., Dehmer, M., and Jurisica, I., Knowledge discovery and interactive data mining in bioinformatics-state-of-the-art, future challenges and research directions. BMC Bioinf 15(6):I1, 2014.","journal-title":"BMC Bioinf"},{"issue":"5","key":"1003_CR114","first-page":"851","volume":"18","author":"S Min","year":"2017","unstructured":"Min, S., Lee, B., and Yoon, S., Deep learning in bioinformatics. Brief. Bioinform. 18(5):851\u2013869, 2017.","journal-title":"Brief. Bioinform."},{"issue":"10","key":"1003_CR115","doi-asserted-by":"publisher","first-page":"244","DOI":"10.3390\/sym9100244","volume":"9","author":"K Lan","year":"2017","unstructured":"Lan, K., Fong, S., Song, W., Vasilakos, A. V., and Millham, R. C., Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring. Symmetry 9(10):244, 2017.","journal-title":"Symmetry"},{"key":"1003_CR116","unstructured":"Kashyap, H., Ahmed, H. A., Hoque, N., Roy, S., and Bhattacharyya, D. K., Big data analytics in bioinformatics: A machine learning perspective. arXiv preprint arXiv:150605101, 2015."},{"key":"1003_CR117","unstructured":"Holzinger, A., and Jurisica I., Knowledge discovery and data mining in biomedical informatics: The future is in integrative, interactive machine learning solutions. In: Interactive knowledge discovery and data mining in biomedical informatics. Springer, pp 1\u201318, 2014."},{"key":"1003_CR118","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.cmpb.2016.04.005","volume":"131","author":"S Kamal","year":"2016","unstructured":"Kamal, S., Ripon, S. H., Dey, N., Ashour, A. S., and Santhi, V., A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset. Comput. Methods Prog. Biomed. 131:191\u2013206, 2016.","journal-title":"Comput. Methods Prog. Biomed."},{"key":"1003_CR119","doi-asserted-by":"crossref","unstructured":"Bhatt, C., Dey, N., and Ashour, A. S., Internet of things and big data technologies for next generation healthcare, 2017.","DOI":"10.1007\/978-3-319-49736-5"},{"key":"1003_CR120","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-60435-0","volume-title":"Internet of Things and Big Data Analytics Toward Next-Generation Intelligence","author":"N Dey","year":"2018","unstructured":"Dey, N., Hassanien, A. E., Bhatt, C., Ashour, A., and Satapathy, S. C., Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Berlin: Springer, 2018."},{"key":"1003_CR121","doi-asserted-by":"crossref","unstructured":"Tamane, S., Tamane, S., Solanki, V. K., and Dey, N., Privacy and security policies in big data, 2017.","DOI":"10.4018\/978-1-5225-2486-1"},{"key":"1003_CR122","doi-asserted-by":"crossref","unstructured":"Dey, N., Bhatt, C., and Ashour, A. S., Big Data for Remote Sensing: Visualization, Analysis and Interpretation, 2018.","DOI":"10.1007\/978-3-319-89923-7"},{"key":"1003_CR123","unstructured":"Kamal, M. S., Dey, N., and Ashour, A. S., Large Scale Medical Data Mining for Accurate Diagnosis: A Blueprint. In Handbook of Large-Scale Distributed Computing in Smart Healthcare (pp. 157\u2013176). Springer: Cham, 2017."},{"issue":"2","key":"1003_CR124","doi-asserted-by":"publisher","first-page":"88","DOI":"10.4018\/IJACI.2017040106","volume":"8","author":"G Manogaran","year":"2017","unstructured":"Manogaran, G., and Lopez, D., Disease surveillance system for big climate data processing and dengue transmission. International Journal of Ambient Computing and Intelligence (IJACI) 8(2):88\u2013105, 2017.","journal-title":"International Journal of Ambient Computing and Intelligence (IJACI)"},{"issue":"4","key":"1003_CR125","doi-asserted-by":"publisher","first-page":"19","DOI":"10.4018\/IJACI.2017100102","volume":"8","author":"A Jain","year":"2017","unstructured":"Jain, A., and Bhatnagar, V., Concoction of Ambient Intelligence and Big Data for Better Patient Ministration Services. International Journal of Ambient Computing and Intelligence (IJACI) 8(4):19\u201330, 2017.","journal-title":"International Journal of Ambient Computing and Intelligence (IJACI)"},{"issue":"4","key":"1003_CR126","doi-asserted-by":"publisher","first-page":"31","DOI":"10.4018\/IJACI.2017100103","volume":"8","author":"H Matallah","year":"2017","unstructured":"Matallah, H., Belalem, G., and Bouamrane, K., Towards a New Model of Storage and Access to Data in Big Data and Cloud Computing. International Journal of Ambient Computing and Intelligence (IJACI) 8(4):31\u201344, 2017.","journal-title":"International Journal of Ambient Computing and Intelligence (IJACI)"},{"issue":"3","key":"1003_CR127","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4018\/IJACI.2018070102","volume":"9","author":"S Vengadeswaran","year":"2018","unstructured":"Vengadeswaran, S., and Balasundaram, S. R., An Optimal Data Placement Strategy for Improving System Performance of Massive Data Applications Using Graph Clustering. International Journal of Ambient Computing and Intelligence (IJACI) 9(3):15\u201330, 2018.","journal-title":"International Journal of Ambient Computing and Intelligence (IJACI)"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10916-018-1003-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-1003-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-1003-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T11:32:19Z","timestamp":1693740739000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10916-018-1003-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,28]]},"references-count":127,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["1003"],"URL":"https:\/\/doi.org\/10.1007\/s10916-018-1003-9","relation":{},"ISSN":["0148-5598","1573-689X"],"issn-type":[{"value":"0148-5598","type":"print"},{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,28]]},"assertion":[{"value":"28 March 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that this article content has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"139"}}