利用神经网络预测蛋白质的亚细胞定位。
Using neural networks for prediction of the subcellular location of proteins.
作者信息
Reinhardt A, Hubbard T
机构信息
The Sanger Centre, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.
出版信息
Nucleic Acids Res. 1998 May 1;26(9):2230-6. doi: 10.1093/nar/26.9.2230.
Abstract
Neural networks have been trained to predict the subcellular location of proteins in prokaryotic or eukaryotic cells from their amino acid composition. For three possible subcellular locations in prokaryotic organisms a prediction accuracy of 81% can be achieved. Assigning a reliability index, 33% of the predictions can be made with an accuracy of 91%. For eukaryotic proteins (excluding plant sequences) an overall prediction accuracy of 66% for four locations was achieved, with 33% of the sequences being predicted with an accuracy of 82% or better. With the subcellular location restricting a protein's possible function, this method should be a useful tool for the systematic analysis of genome data and is available via a server on the world wide web.
摘要
神经网络已被训练用于根据蛋白质的氨基酸组成预测其在原核生物或真核生物细胞中的亚细胞定位。对于原核生物中三种可能的亚细胞定位，预测准确率可达81%。若赋予可靠性指标，33%的预测准确率可达91%。对于真核生物蛋白质（不包括植物序列），四个定位的总体预测准确率为66%，其中33%的序列预测准确率达到82%或更高。由于亚细胞定位限制了蛋白质的可能功能，该方法应是系统分析基因组数据的有用工具，可通过万维网上的服务器获取。
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