Gray BP neural network based prediction of rice protein interaction network.

doi:doi.org/10.1007/s10586-017-1663-0

	Gray BP neural network based prediction of rice protein interaction network.
	Wang X(王雪)
刊名	Cluster Computing
	2018-01-05
关键词	Gray value BP neural network Rice protein interaction Prediction
DOI	doi.org/10.1007/s10586-017-1663-0
英文摘要	In order to improve the effectiveness of the network prediction result of rice protein interaction, the network prediction method of rice protein interaction based on gray BP neural network has been proposed. Firstly, a series of key features about interaction sites of rice protein such as the series spectrum, conserved weight, entropy value, accessible exterior product of compound and sequence rate, etc., should be extracted fifirstly. Then the gray BP neural network and their integration will be applied to the training and test of these sample set. Ten times of cross validation are applied to the training and test, and four groups of feature combinations of rice protein interaction with comparability are created. As for each time of the addition of new features in the experiment, the accurate prediction rate would be improved, especially in case of the addition of exterior product and sequence rate features, the accuracy can be improved greatly, which means that in case the combination of multiple features is adopted, the method of predicting the interaction of rice protein by combining the gray BP neural network algorithm is accurate and effective.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/126103]
专题	中国科学院合肥物质科学研究院
作者单位	Institute of Technical Biology & Agriculture Engineering, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Wang X. Gray BP neural network based prediction of rice protein interaction network.[J]. Cluster Computing,2018.
APA	Wang X.(2018).Gray BP neural network based prediction of rice protein interaction network..Cluster Computing.
MLA	Wang X."Gray BP neural network based prediction of rice protein interaction network.".Cluster Computing (2018).