Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
A review of feature selection techniques in bioinformatics
Bioinformatics
Informative MicroRNA expression patterns for cancer classification
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
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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 the test data.