{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T17:03:04Z","timestamp":1725987784376},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319962917"},{"type":"electronic","value":"9783319962924"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-96292-4_17","type":"book-chapter","created":{"date-parts":[[2018,8,13]],"date-time":"2018-08-13T14:29:34Z","timestamp":1534170574000},"page":"210-221","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advanced Machine Learning Models for Large Scale Gene Expression Analysis in Cancer Classification: Deep Learning Versus Classical Models"],"prefix":"10.1007","author":[{"given":"Imene","family":"Zenbout","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Souham","family":"Meshoul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,8,14]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Bumgarner, R.: Overview of DNA microarrays: types, applications, and their future. Curr. Protoc. Mol. Biol. 22.1.1\u201322.1.11 (2013)","DOI":"10.1002\/0471142727.mb2201s101"},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-38951-1_1","volume-title":"Basics of Bioinformatics","author":"X Zhang","year":"2013","unstructured":"Zhang, X., Zhou, X., Wang, X.: Basics for bioinformatics. In: Jiang, R., Zhang, X., Zhang, M.Q. (eds.) Basics of Bioinformatics, pp. 1\u201325. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38951-1_1"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/978-1-4939-1381-7_2","volume-title":"Cancer Bioinformatics","author":"Y Xu","year":"2014","unstructured":"Xu, Y., Cui, J., Puett, D.: Omic data, information derivable and computational needs. In: Xu, Y., Cui, J., Puett, D. (eds.) Cancer Bioinformatics, pp. 41\u201363. Springer, New York (2014). https:\/\/doi.org\/10.1007\/978-1-4939-1381-7_2"},{"issue":"3","key":"17_CR4","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/S1369-5274(00)00091-6","volume":"3","author":"CA Harrington","year":"2000","unstructured":"Harrington, C.A., Rosenow, C., Retief, J.: Monitoring gene expression using dna microarrays. Curr. Opin. Microbiol. 3(3), 285\u2013291 (2000)","journal-title":"Curr. Opin. Microbiol."},{"key":"17_CR5","doi-asserted-by":"publisher","first-page":"01","DOI":"10.18642\/ijamml_7100121446","volume":"2","author":"A Bhola","year":"2015","unstructured":"Bhola, A., Tiwari, A.: Machine learning based approaches for cancer classification using gene expression data. Mach. Learn. Appl.: Int. J. 2, 01\u201312 (2015)","journal-title":"Mach. Learn. Appl.: Int. J."},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Kriti, Virmani, J., Agarwal, R.: Evaluating the efficacy of gabor features in the discrimination of breast density patterns using various classifiers. In: Dey, N., Ashour, A., Borra, S. (eds.) Classification in BioApps, LNCVB, vol. 26, pp. 105\u2013131. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-65981-7_5","DOI":"10.1007\/978-3-319-65981-7_5"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-319-20010-1_3","volume-title":"An Introduction to Machine Learning","author":"M Kubat","year":"2015","unstructured":"Kubat, M.: Similarities: nearest-neighbor classifiers. An Introduction to Machine Learning, pp. 43\u201364. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20010-1_3"},{"issue":"3","key":"17_CR8","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"17_CR9","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-94-007-6886-4_15","volume-title":"Machine Learning in Medicine","author":"TJ Cleophas","year":"2013","unstructured":"Cleophas, T.J., Zwinderman, A.H.: Support vector machines. In: Cleophas, T.J., Zwinderman, A.H. (eds.) Machine Learning in Medicine, pp. 155\u2013161. Springer, Dordrecht (2013). https:\/\/doi.org\/10.1007\/978-94-007-6886-4_15"},{"issue":"Supplement C","key":"17_CR10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.procs.2015.03.178","volume":"47","author":"CDA Vanitha","year":"2015","unstructured":"Vanitha, C.D.A., Devaraj, D., Venkatesulu, M.: Gene expression data classification using support vector machine and mutual information-based gene selection. Procedia Comput. Sci. 47(Supplement C), 13\u201321 (2015). Graph Algorithms, High Performance Implementations and Its Applications (ICGHIA 2014)","journal-title":"Procedia Comput. Sci."},{"key":"17_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-319-20010-1_4","volume-title":"An Introduction to Machine Learning","author":"M Kubat","year":"2015","unstructured":"Kubat, M.: Inter-class boundaries: linear and polynomial classifiers. An Introduction to Machine Learning, pp. 65\u201390. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20010-1_4"},{"key":"17_CR12","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding Machine Learning: From Theory to Algorithms","author":"S Shalev-Shwartz","year":"2014","unstructured":"Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, New York (2014)"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"An, Y., Sun, S., Wang, S.: Naive Bayes classifiers for music emotion classification based on lyrics. In: 2017 IEEE\/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 635\u2013638, May 2017","DOI":"10.1109\/ICIS.2017.7960070"},{"key":"17_CR14","unstructured":"McCallum, A., Nigam, K., et al.: A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, Madison, WI, vol. 752, pp. 41\u201348 (1998)"},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"7716","DOI":"10.1109\/ACCESS.2016.2585661","volume":"4","author":"A Sharmila","year":"2016","unstructured":"Sharmila, A., Geethanjali, P.: Dwt based detection of epileptic seizure from EEG signals using naive bayes and k-NN classifiers. IEEE Access 4, 7716\u20137727 (2016)","journal-title":"IEEE Access"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Karthick, G., Harikumar, R.: Comparative performance analysis of Naive Bayes and SVM classifier for oral X-ray images. In: 2017 4th International Conference on Electronics and Communication Systems (ICECS), pp. 88\u201392, February 2017","DOI":"10.1109\/ECS.2017.8067843"},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"L Yann","year":"2015","unstructured":"Yann, L., Yoshua, B., Geoffrey, H.: Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Min, S., Lee, B., Yoon, S.: Deep Learning in Bioinformatics. ArXiv e-prints, March 2016","DOI":"10.1093\/bib\/bbw068"},{"issue":"C","key":"17_CR19","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1016\/j.procs.2016.05.512","volume":"80","author":"M Elleuch","year":"2016","unstructured":"Elleuch, M., Maalej, R., Kherallah, M.: A new design based-SVM of the CNN classifier architecture with dropout for offline arabic handwritten recognition. Procedia Comput. Sci. 80(C), 1712\u20131723 (2016)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"17_CR20","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1073\/pnas.95.1.334","volume":"95","author":"X Wen","year":"1998","unstructured":"Wen, X., Fuhrman, S., Michaels, G.S., Carr, D.B., Smith, S., Barker, J.L., Somogyi, R.: Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. Sci. 95(1), 334\u2013339 (1998)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"8","key":"17_CR21","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33(8), 831\u2013838 (2015)","journal-title":"Nat. Biotechnol."},{"key":"17_CR22","first-page":"42","volume":"3","author":"GS Michaels","year":"1998","unstructured":"Michaels, G.S., Carr, D.B., Askenazi, M., Fuhrman, S., Wen, X., Somogyi, R.: Cluster analysis and data visualization of large-scale gene expression data. Pac. Symp. Biocomput. 3, 42\u201353 (1998)","journal-title":"Pac. Symp. Biocomput."},{"issue":"6","key":"17_CR23","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/89044","volume":"7","author":"J Khan","year":"2001","unstructured":"Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7(6), 673\u2013679 (2001)","journal-title":"Nat. Med."},{"issue":"8","key":"17_CR24","doi-asserted-by":"publisher","first-page":"727","DOI":"10.2174\/1386207013330733","volume":"4","author":"L Li","year":"2001","unstructured":"Li, L., Darden, T.A., Weingberg, C., Levine, A., Pedersen, L.G.: Gene assessment and sample classification for gene expression data using a genetic algorithm\/k-nearest neighbor method. Comb. Chem. High Throughput Screen. 4(8), 727\u2013739 (2001)","journal-title":"Comb. Chem. High Throughput Screen."},{"issue":"1","key":"17_CR25","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1186\/s12864-017-3906-0","volume":"18","author":"Y Li","year":"2017","unstructured":"Li, Y., Kang, K., Krahn, J.M., Croutwater, N., Lee, K., Umbach, D.M., Li, L.: A comprehensive genomic pan-cancer classification using the cancer genome atlas gene expression data. BMC Genomics 18(1), 508 (2017)","journal-title":"BMC Genomics"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Begum, S., Chakraborty, D., Sarkar, R.: Cancer classification from gene expression based microarray data using SVM ensemble. In: 2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), pp. 13\u201316, December 2015","DOI":"10.1109\/CATCON.2015.7449500"},{"key":"17_CR27","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/978-3-319-19066-2_45","volume-title":"Current Approaches in Applied Artificial Intelligence","author":"JC Ang","year":"2015","unstructured":"Ang, J.C., Haron, H., Hamed, H.N.A.: Semi-supervised SVM-based feature selection for cancer classification using microarray gene expression data. In: Ali, M., Kwon, Y.S., Lee, C.-H., Kim, J., Kim, Y. (eds.) IEA\/AIE 2015. LNCS (LNAI), vol. 9101, pp. 468\u2013477. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19066-2_45"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhao, H., Shen, J., Zhou, R., Zhou, Q.: Supervised machine learning model for high dimensional gene data in colon cancer detection. In: 2015 IEEE International Congress on Big Data, pp. 134\u2013141, June 2015","DOI":"10.1109\/BigDataCongress.2015.28"},{"key":"17_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/978-3-319-59147-6_5","volume-title":"Advances in Computational Intelligence","author":"D Urda","year":"2017","unstructured":"Urda, D., Montes-Torres, J., Moreno, F., Franco, L., Jerez, J.M.: Deep learning to analyze RNA-seq gene expression data. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2017. LNCS, vol. 10306, pp. 50\u201359. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59147-6_5"},{"key":"17_CR30","unstructured":"Fakoor, R., Ladhak, F., Nazi, A., Huber, M.: Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of the International Conference on Machine Learning (2013)"},{"key":"17_CR31","unstructured":"Bhat, R.R., Viswanath, V., Li, X.: Deepcancer: detecting cancer through gene expressions via deep generative learning. CoRR abs\/1612.03211 (2016)"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Danaee, P., Ghaeini, R., Hendrix, D.A.: A deep learning approach for cancer detection and relevent gene identification, pp. 219\u2013229. World Scientific (2016)","DOI":"10.1142\/9789813207813_0022"},{"key":"17_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2017.09.005","volume":"153","author":"Y Xiao","year":"2018","unstructured":"Xiao, Y., Wu, J., Lin, Z., Zhao, X.: A deep learning-based multi-model ensemble method for cancer prediction. Comput. Methods Programs Biomed. 153, 1\u20139 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"5","key":"17_CR34","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1182\/blood-2008-10-187203","volume":"114","author":"KI Mills","year":"2009","unstructured":"Mills, K.I., Kohlmann, A., Williams, P.M., Wieczorek, L., Liu, W.M., Li, R., Wei, W., Bowen, D.T., Loeffler, H., Hernandez, J.M., Hofmann, W.K., Haferlach, T.: Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome. Blood 114(5), 1063\u20131072 (2009)","journal-title":"Blood"},{"issue":"3","key":"17_CR35","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1007\/s10549-013-2501-6","volume":"138","author":"WA Woodward","year":"2013","unstructured":"Woodward, W.A., Krishnamurthy, S., Yamauchi, H., El-Zein, R., Ogura, D., Kitadai, E., Niwa, S.I., Cristofanilli, M., Vermeulen, P., Dirix, L., Viens, P., van Laere, S., Bertucci, F., Reuben, J.M., Ueno, N.T.: Genomic and expression analysis of microdissected inflammatory breast cancer. Breast Cancer Res. Treat. 138(3), 761\u2013772 (2013)","journal-title":"Breast Cancer Res. Treat."},{"issue":"1","key":"17_CR36","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.lungcan.2011.05.028","volume":"75","author":"T Fujiwara","year":"2012","unstructured":"Fujiwara, T., Hiramatsu, M., Isagawa, T., Ninomiya, H., Inamura, K., Ishikawa, S., Ushijima, M., Matsuura, M., Jones, M.H., Shimane, M., Nomura, H., Ishikawa, Y., Aburatani, H.: ASCL1-coexpression profiling but not single gene expression profiling defines lung adenocarcinomas of neuroendocrine nature with poor prognosis. Lung Cancer 75(1), 119\u2013125 (2012)","journal-title":"Lung Cancer"},{"issue":"12","key":"17_CR37","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.1158\/1055-9965.EPI-12-0428","volume":"21","author":"V Urquidi","year":"2012","unstructured":"Urquidi, V., Goodison, S., Cai, Y., Sun, Y., Rosser, C.J.: A candidate molecular biomarker panel for the detection of bladder cancer. Cancer Epidemiol. Prev. Biomark. 21(12), 2149\u20132158 (2012)","journal-title":"Cancer Epidemiol. Prev. Biomark."},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Wojtas, B., Pfeifer, A., Oczko-Wojciechowska, M., Krajewska, J., Czarniecka, A., Kukulska, A., Eszlinger, M., Musholt, T., Stokowy, T., Swierniak, M., Stobiecka, E., Chmielik, E., Rusinek, D., Tyszkiewicz, T., Halczok, M., Hauptmann, S., Lange, D., Jarzab, M., Paschke, R., Jarzab, B.: Gene expression (mRNA) markers for differentiating between malignant and benign follicular thyroid tumours. Int. J. Mol. Sci. 18(6) (2017)","DOI":"10.3390\/ijms18061184"}],"container-title":["Communications in Computer and Information Science","Big Data, Cloud and Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-96292-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T04:31:09Z","timestamp":1661747469000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-96292-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319962917","9783319962924"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-96292-4_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2018]]}}}