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Large-Scale Scene Recognition Based on Extreme Learning Machines Read chapter Read first chapter

2016 | OriginalPaper | Chapter

RNA Secondary Structure Prediction Using Extreme Learning Machine with Clustering Under-Sampling Technique

Authors : Tianhang Liu, Jiarun Lin, Chengkun Wu, Jianping Yin

Published in: Proceedings of ELM-2015 Volume 2

Publisher: Springer International Publishing

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Abstract

This paper gives a machine learning method for the subject of RNA secondary structure prediction. The method is based on extreme learning machine for its outstanding performance in classification problem, and use under-sampling technique to solve the problem of data imbalance. Feature vector in the classifier includes covariation score and inconsistent sequence penalty. The proposed method is compared with SVM and ELM without under-sampling, as well as classical method RNAalifold in terms of sensitivity, specificity, Matthews correlation coefficient and G-mean. The training and testing data are 68 RNA aligned families from Rfam, version 11.0. The results show that the proposed method can achieve highest scores in sensitivity, MCC and G-mean, which means that it is an effective method for RNA secondary structure prediction.

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Metadata
Title
RNA Secondary Structure Prediction Using Extreme Learning Machine with Clustering Under-Sampling Technique
Authors
Tianhang Liu
Jiarun Lin
Chengkun Wu
Jianping Yin
Copyright Year
2016
Book
Proceedings of ELM-2015 Volume 2

Print ISBN: 978-3-319-28372-2

Electronic ISBN: 978-3-319-28373-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-28373-9_27

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