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A Tool Preference Choice Method for RNA Secondary Structure Prediction by SVM with Statistical Tests.

ABSTRACT: The Prediction of RNA secondary structures has drawn much attention from both biologists and computer scientists. Many useful tools have been developed for this purpose. These tools have their individual strengths and weaknesses. As a result, based on support vector machines (SVM), we propose a tool choice method which integrates three prediction tools: pknotsRG, RNAStructure, and NUPACK. Our method first extracts features from the target RNA sequence, and adopts two information-theoretic feature selection methods for feature ranking. We propose a method to combine feature selection and classifier fusion in an incremental manner. Our test data set contains 720 RNA sequences, where 225 pseudoknotted RNA sequences are obtained from PseudoBase, and 495 nested RNA sequences are obtained from RNA SSTRAND. The method serves as a preprocessing way in analyzing RNA sequences before the RNA secondary structure prediction tools are employed. In addition, the performance of various configurations is subject to statistical tests to examine their significance. The best base-pair accuracy achieved is 75.5%, which is obtained by the proposed incremental method, and is significantly higher than 68.8%, which is associated with the best predictor, pknotsRG.

SUBMITTER: Hor CY

PROVIDER: S-EPMC3629938 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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A Tool Preference Choice Method for RNA Secondary Structure Prediction by SVM with Statistical Tests.

Hor Chiou-Yi CY Yang Chang-Biau CB Chang Chia-Hung CH Tseng Chiou-Ting CT Chen Hung-Hsin HH

Evolutionary bioinformatics online 20130414

The Prediction of RNA secondary structures has drawn much attention from both biologists and computer scientists. Many useful tools have been developed for this purpose. These tools have their individual strengths and weaknesses. As a result, based on support vector machines (SVM), we propose a tool choice method which integrates three prediction tools: pknotsRG, RNAStructure, and NUPACK. Our method first extracts features from the target RNA sequence, and adopts two information-theoretic featur ...[more]

PMID: 23641141

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A Tool Preference Choice Method for RNA Secondary Structure Prediction by SVM with Statistical Tests.

Publications

A Tool Preference Choice Method for RNA Secondary Structure Prediction by SVM with Statistical Tests.

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