Towards a better prediction of subcellular location of long non-coding RNA

Abstract

The spatial distribution pattern of long non-coding RNA (lncRNA) in cell is tightly related to their function. With the increment of publicly available subcellular location data, a number of computational methods have been developed for the recognition of the subcellular localization of lncRNA. Unfortunately, these computational methods suffer from the low discriminative power of redundant features or overfitting of oversampling. To address those issues and enhance the prediction performance, we present a support vector machine-based approach by incorporating mutual information algorithm and incremental feature selection strategy. As a result, the new predictor could achieve the overall accuracy of 91.60%. The highly automated web-tool is available at lin-group.cn/server/iLoc-LncRNA(2.0)/website. It will help to get the knowledge of lncRNA subcellular localization.

Graphical abstract

Keywords

lncRNA / subcellular localization / support vector machine / mutual information / Web server

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Acknowledgements

This work was supported by the National Nature Scientific Foundation of China (Grant No. 61772119), Sichuan Provincial Science Fund for Distinguished Young Scholars (2020JDJQ0012).

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