基于神经网络的酵母基因组亚细胞定位预测系统
A System for Predicting Subcellular Localization of Yeast Genome Using Neural Network
摘要：
蛋白质的亚细胞位置可以提供关于其功能的有价值的信息。随着测序基因组数据的快速增长，对自动化和准确的工具来预测亚细胞定位的需求变得越来越重要。人们已经做了很多努力来预测蛋白质的亚细胞定位。本文旨在将人工神经网络与生物信息学相结合，预测蛋白质在酵母基因组中的位置。我们介绍了一种新的基于反向传播神经网络的亚细胞预测方法。结果表明，该系统的预测误差在5 ~ 10%以内。
英文摘要：
The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important. Many efforts have been made to predict protein subcellular localization. This paper aims to merge the artificial neural networks and bioinformatics to predict the location of protein in yeast genome. We introduce a new subcellular prediction method based on a backpropagation neural network. The results show that the prediction within an error limit of 5 to 10 percentage can be achieved with the system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
