Predicting A-to-I RNA editing by feature selection and random forest.

RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest metho...

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Autores principales:	Yang Shu, Ning Zhang, Xiangyin Kong, Tao Huang, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4989cf82814b46c2b66c9ac06ac2711e
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id	oai:doaj.org-article:4989cf82814b46c2b66c9ac06ac2711e
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spelling	oai:doaj.org-article:4989cf82814b46c2b66c9ac06ac2711e2021-11-25T05:55:30ZPredicting A-to-I RNA editing by feature selection and random forest.1932-620310.1371/journal.pone.0110607https://doaj.org/article/4989cf82814b46c2b66c9ac06ac2711e2014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0110607https://doaj.org/toc/1932-6203RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and MCC (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and MCC were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.Yang ShuNing ZhangXiangyin KongTao HuangYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e110607 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yang Shu Ning Zhang Xiangyin Kong Tao Huang Yu-Dong Cai Predicting A-to-I RNA editing by feature selection and random forest.
description	RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and MCC (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and MCC were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.
format	article
author	Yang Shu Ning Zhang Xiangyin Kong Tao Huang Yu-Dong Cai
author_facet	Yang Shu Ning Zhang Xiangyin Kong Tao Huang Yu-Dong Cai
author_sort	Yang Shu
title	Predicting A-to-I RNA editing by feature selection and random forest.
title_short	Predicting A-to-I RNA editing by feature selection and random forest.
title_full	Predicting A-to-I RNA editing by feature selection and random forest.
title_fullStr	Predicting A-to-I RNA editing by feature selection and random forest.
title_full_unstemmed	Predicting A-to-I RNA editing by feature selection and random forest.
title_sort	predicting a-to-i rna editing by feature selection and random forest.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/4989cf82814b46c2b66c9ac06ac2711e
work_keys_str_mv	AT yangshu predictingatoirnaeditingbyfeatureselectionandrandomforest AT ningzhang predictingatoirnaeditingbyfeatureselectionandrandomforest AT xiangyinkong predictingatoirnaeditingbyfeatureselectionandrandomforest AT taohuang predictingatoirnaeditingbyfeatureselectionandrandomforest AT yudongcai predictingatoirnaeditingbyfeatureselectionandrandomforest
_version_	1718414396865118208

Predicting A-to-I RNA editing by feature selection and random forest.

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