计算机应用与软件2017，Vol.34Issue(9)：183-187,5.DOI:10.3969/j.issn.1000-386x.2017.09.037
基于深度学习的PD致病基因活性预测
PREDICTION OF PD DISEASE GENE ACTIVITY BASED ON DEEP LEARNING
摘要
Abstract
Parkinson's disease (PD) is a kind of nerve system disease,more common in the elderly.At present,the condition of the etiology and pathogenesis is not clear,but according to multinational clinical trial data statistics and analysis,PINKs gene is one of the important reason to influence the whole PD pathogenesis.This paper study for the structure of activity gene,and the DBN and SAE are proposed for the PINKs activity prediction.The proposed algorithm can leam automatically by the characteristics of deep web unit is suitable for the high nonlinear combination classifier classification feature,and will these high-level features inputs to the classifier for data analysis.The experimental results show that the DBN algorithm the average prediction accuracy of SVM with ANN respectively increased by 28.04％,18.84％;SAE algorithm the average prediction accuracy of the SVM and ANN respectively increased by 23.51％,14.31％.In this paper,based on the deep study of PINKs activity prediction method has higher prediction accuracy and stability,in conformity with the theory of distribution are,also is applicable to the activity of research and discussion.关键词
活性/深度学习/SAE/预测/研究Key words
Activity/Deep learning/SAE/Prediction/Research分类
信息技术与安全科学引用本文复制引用
李自臣,田生伟,刘江越,高双印..基于深度学习的PD致病基因活性预测[J].计算机应用与软件,2017,34(9):183-187,5.基金项目
新疆研究生科研创新基金项目(XJGRI2015034). （XJGRI2015034）
