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Int. J. Mol. Sci. 2017, 18(12), 2691; https://doi.org/10.3390/ijms18122691

Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest

1
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China
2
Center of Information Support & Assurance Technology, Anhui University, Hefei 230601, China
3
School of Computer Science and Technology, Anhui University, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Received: 20 September 2017 / Revised: 21 November 2017 / Accepted: 8 December 2017 / Published: 12 December 2017
(This article belongs to the Section Biochemistry)
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Abstract

In bioinformatics, exon skipping (ES) event prediction is an essential part of alternative splicing (AS) event analysis. Although many methods have been developed to predict ES events, a solution has yet to be found. In this study, given the limitations of machine learning algorithms with RNA-Seq data or genome sequences, a new feature, called RS (RNA-seq and sequence) features, was constructed. These features include RNA-Seq features derived from the RNA-Seq data and sequence features derived from genome sequences. We propose a novel Rotation Forest classifier to predict ES events with the RS features (RotaF-RSES). To validate the efficacy of RotaF-RSES, a dataset from two human tissues was used, and RotaF-RSES achieved an accuracy of 98.4%, a specificity of 99.2%, a sensitivity of 94.1%, and an area under the curve (AUC) of 98.6%. When compared to the other available methods, the results indicate that RotaF-RSES is efficient and can predict ES events with RS features. View Full-Text
Keywords: exon skipping event; RNA-Seq data; sequence information exon skipping event; RNA-Seq data; sequence information
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Du, X.; Hu, C.; Yao, Y.; Sun, S.; Zhang, Y. Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest. Int. J. Mol. Sci. 2017, 18, 2691.

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