Using neural networks for prediction of the subcellular location of proteins
Using neural networks for prediction of the subcellular location of proteins
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DOI：
10.1093/nar/26.9.2230
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发表时间：
1998-05-01
影响因子：
14.9
通讯作者：
Hubbard, T
中科院分区：
文献类型：
--
作者：
Reinhardt, A;Hubbard, T
神经网络经过训练，可以根据氨基酸组成来预测原核或真核细胞中蛋白质的亚细胞位置。对于原核生物中三个可能的亚细胞位置，预测准确度可以达到 81%。指定一个可靠性指标，可以做出 33% 的预测，准确率达到 91%。对于真核蛋白质（不包括植物序列），四个位置的总体预测准确度达到 66%，其中 33% 的序列预测准确度达到 82% 或更高。由于亚细胞位置限制了蛋白质的可能功能，因此该方法应该成为基因组数据系统分析的有用工具，并且可以通过万维网上的服务器获得。
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.