Gray BP neural network based prediction of rice protein interaction network.
文献类型：期刊论文
| 作者 | Wang X(王雪)
|
| 刊名 | Cluster Computing
 |
| 出版日期 | 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 |
| 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
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