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. 2017 Sep 12;7:11347. doi: 10.1038/s41598-017-11665-4

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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With an increase in the number of samples for training, the percentage of mis-classifications for HARF, Decision Tree and Linear Discriminant Analysis (LDA) all get reduced. Using drug Nilotinib of CCLE database and 2 cancer types HLT and Lung, this reduction of misclassification is shown. For small number of samples, HARF has the lowest misclassification rate. For large sample sizes, LDA gives the lowest misclassification rate, but the differences are minimal in both the cases.