NDLI: miRNA-dis: microRNA precursor identification based on distance structure status pairs.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Liu, Bin Fang, Longyun Chen, Junjie Liu, Fule Wang, Xiaolong
Description	Author Affiliation: Liu B ( School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055, China. bliu@insun.hit.edu.cn dragoncloudest@gmail.com chenjunjie@hitsz.edu.cn liufule12@gmail.com wangxl@insun.hit.edu.cn.)
Abstract	MicroRNA precursor identification is an important task in bioinformatics. Support Vector Machine (SVM) is one of the most effective machine learning methods used in this field. The performance of SVM-based methods depends on the vector representations of RNAs. However, the discriminative power of the existing feature vectors is limited, and many methods lack an interpretable model for analysis of characteristic sequence features. Prior studies have demonstrated that sequence or structure order effects were relevant for discrimination, but little work has explored how to use this kind of information for human pre-microRNA identification. In this study, in order to incorporate the structure-order information into the prediction, a method called 'miRNA-dis' was proposed, in which the feature vector was constructed by the occurrence frequency of the 'distance structure status pair' or just the 'distance-pair'. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the miRNA-dis outperformed some state-of-the-art predictors in this area. Remarkably, miRNA-dis trained with human data can correctly predict 87.02% of the 4022 pre-miRNAs from 11 different species ranging from animals, plants and viruses. miRNA-dis would be a useful high throughput tool for large-scale analysis of microRNA precursors. In addition, the learnt model can be easily analyzed in terms of discriminative features, and some interesting patterns were discovered, which could reflect the characteristics of microRNAs. A user-friendly web-server of miRNA-dis was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/miRNA-dis/.
File Format	HTM / HTML
ISSN	1742206X
Issue Number	4
Volume Number	11
e-ISSN	17422051
Journal	Molecular BioSystems
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2015-04-01
Publisher Place	Great Britain (UK)
Access Restriction	Subscribed
Subject Keyword	Discipline Molecular Biology Discipline Biochemistry Computational Biology Methods Micrornas Chemistry Genetics User-computer Interface Animals Databases, Genetic Humans Internet Rna, Plant Rna, Viral Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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