S^2FS : Single Score Feature Selection Applied to the Problem of Distinguishing Long Non-coding RNAs from Protein Coding Transcripts

The task of distinguishing long non-coding RNAs (lncRNAs) from protein coding transcripts (PCTs) has been previously addressed with machine learning (ML) algorithms using hundreds of features. However, the use of a large number of features can negatively affect the predictive performance of these algorithms since it can lead to problems like overfitting due to a phenomenon known as the curse of dimensionality. In order to deal with these problems, dimensionality reduction techniques have been proposed, among them, feature selection. This work proposes and experimentally evaluates a simple and fast feature selection technique, called Single Score Feature Selection - \(S^2FS\).

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