Identify Huntington’s disease associated genes based on restricted Boltzmann machine with RNA-seq data

BMC Bioinformatics

Research Article

Xiongwen Quan¹  Feng Duan¹  Xue Jiang¹  Han Zhang¹ 

[1] College of Computer and Control Engineering, Nankai University, Tongyan Road, 300350, Tianjin, China;Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, 300350, Tianjin, China;

关键词: Restricted Boltzmann machine;    Key genes associated to the disease progression;    Huntington’s disease;    RNA-seq data;   

DOI  :  10.1186/s12859-017-1859-6

 received in 2017-05-30, accepted in 2017-10-02,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundPredicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression.ResultsIn this paper, we propose a deep learning approach based on the restricted Boltzmann machine to analyze the RNA-seq data of Huntington’s disease, namely stacked restricted Boltzmann machine (SRBM). According to the SRBM, we also design a novel framework to screen the key genes during the Huntington’s disease development. In this work, we assume that the effects of regulatory factors can be captured by the hierarchical structure and narrow hidden layers of the SRBM. First, we select disease-associated factors with different time period datasets according to the differentially activated neurons in hidden layers. Then, we select disease-associated genes according to the changes of the gene energy in SRBM at different time periods.ConclusionsThe experimental results demonstrate that SRBM can detect the important information for differential analysis of time series gene expression datasets. The identification accuracy of the disease-associated genes is improved to some extent using the novel framework. Moreover, the prediction precision of disease-associated genes for top ranking genes using SRBM is effectively improved compared with that of the state of the art methods.

【 授权许可】

CC BY   
© The Author(s) 2017

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