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Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting...
A number of attempts to classify cancer samples using miRNA/gene expression profiles are known in literature. However, semi-supervised learning models have only been recently introduced to exploit the huge unlabeled expression profiles in enhancing sample classification. It is important to combine both miRNA and gene expression sets as that provides more information on the characteristics of cancer...
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