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ICONIP
2010

Exploring Features and Classifiers to Classify MicroRNA Expression Profiles of Human Cancer

9 years 4 months ago
Exploring Features and Classifiers to Classify MicroRNA Expression Profiles of Human Cancer
Recently, some non-coding small RNAs, known as microRNAs (miRNA), have drawn a lot of attention to identify their role in gene regulation and various biological processes. The miRNA profiles are surprisingly informative, reflecting the malignancy state of the tissues. In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers. Here we use the expression profile of 217 miRNAs from 186 samples, including multiple human cancers. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, support vector machine, and knearest neighbor have been used for classification. Experimental results indicate that k-nearest neighbor with cosine coefficient produces the best result, 95.0% of recognition rate on ...
Kyung-Joong Kim, Sung-Bae Cho
Added 04 Mar 2011
Updated 04 Mar 2011
Type Journal
Year 2010
Where ICONIP
Authors Kyung-Joong Kim, Sung-Bae Cho
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