Dataset Information

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.

ABSTRACT: BACKGROUND:The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions. RESULTS:In this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k-mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods. CONCLUSIONS:The results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It's anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.

SUBMITTER: Yi HC

PROVIDER: S-EPMC7029608 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.

Yi Hai-Cheng HC You Zhu-Hong ZH Wang Mei-Neng MN Guo Zhen-Hao ZH Wang Yan-Bin YB Zhou Ji-Ren JR

BMC bioinformatics 20200218 1

<h4>Background</h4>The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions.<h4>Results</h4>In this work, we develop a stacking ensemble computational framework, RPI-SE, for ...[more]

PMID: 32070279

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Dataset Information

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.

Publications

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.

Similar Datasets

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