一种在不同物种知识库中鉴定前microrna的新方法

Tianyi Zhao, Ningyi Zhang, Jun Ren, Peigang Xu, Zhiyan Liu, Liang Cheng, Yang Hu
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引用次数: 1

摘要

超过1/3的人类基因受microrna调控。microRNA (miRNA)的鉴定是发现miRNA的调控机制和开展遗传病治疗的前提。传统的鉴定方法是生物实验,但存在周期长、成本高、缺失只存在于特定时期或低表达水平的mirna等缺陷。因此,为了克服这些缺陷,我们采用机器学习方法来识别mirna。在本研究中,为了识别真实和伪mirna并对不同物种进行分类,我们基于一级和二级结构提取了98个维度的特征,然后我们提出了BP- adaboost方法,通过构建多个BP神经网络分类器并对这些分类器分配权重来找出BP神经网络的过拟合现象。提出的新方法提高了检测的精度和稳定性。在本研究中,我们通过实验验证了该方法的有效性和优越性。
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A novel method to identify pre-microRNA in various species knowledge base
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 only exist in a specific period or low expression level. Therefore, to overcome these defects, machine learning method is applied to identify miRNAs. 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 raised the accuracy and the stability. In this study, we verified the effectiveness and superiority over other methods by experiments.
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