A novel method to identify pre-microRNA in various species knowledge base on various species

Journal of Biomedical Semantics

Liang Cheng¹  Zhiyan Liu²  Peigang Xu²  Tianyi Zhao²  Ningyi Zhang²  Ying Zhang³  Yang Hu⁴  Jun Ren⁴ 

[1] College of Bioinformatics Science and Technology, Harbin Medical University;Department of Computer Science and Technology, Harbin Institute of Technology;Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital;School of Life Science and Technology, Harbin Institute of Technology;

关键词: Pre-miRNA identification;    BP neural network;    Adaboost;   

DOI  :  10.1186/s13326-017-0143-z

来源: DOAJ

【 摘 要 】

Abstract Background More than 1/3 of human genes are regulated by microRNAs. The identification of microRNA (miRNA) is the precondition of discovering the regulatory mechanism of miRNA and developing the cure for genetic diseases. The traditional identification method is biological experiment, but it has the defects of long period, high cost, and missing the miRNAs that but also many other algorithms only exist in a specific period or low expression level. Therefore, to overcome these defects, machine learning method is applied to identify miRNAs. Results In this study, for identifying real and pseudo miRNAs and classifying different species, we extracted 98 dimensional features based on the primary and secondary structure, then we proposed the BP-Adaboost method to figure out the overfitting phenomenon of BP neural network by constructing multiple BP neural network classifiers and distributed weights to these classifiers. The novel method we proposed, from the 4 evaluation terms, have achieved greatly improvement on the effect of identifying true pre-RNA compared to other methods. And from the respect of identifying species of pre-RNA, the novel method achieved more accuracy than other algorithms. Conclusions The BP-Adaboost method has achieved more than 98% accuracy in identifying real and pseudo miRNAs. It is much higher than not only BP but also many other algorithms. In the second experiment, restricted by the data, the algorithm could not get high accuracy in identifying 7 species, but also better than other algorithms.

【 授权许可】

Unknown