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Using Neural Networks for Prediction of Subcellular Location of Prokaryotic and Eukaryotic Proteins

机译:使用神经网络预测原核和真核蛋白质的亚细胞定位

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T. Kohonen's self-organization model, a typical neural network model, was applied to predict the subcellular location of proteins from their amino acid composition. The reinhardt and Hubbard database was used to examine the performance of the neural network method. The rates of correct prediction for the three possible subcellular location of prokaryotic proteins were 96.1% by the self-consistency test and 84.4% by the jackknife test. The rates of correct prediction for the four possible subcellular olcation of eukaryotic proteins were 95.6% by the self-consistency test and 70.6% by the jackknife test.
机译:T. Kohonen的自组织模型(一种典型的神经网络模型)被用于根据蛋白质的氨基酸组成预测蛋白质的亚细胞位置。使用reinhardt和Hubbard数据库来检查神经网络方法的性能。通过自我一致性测试,对原核蛋白三个可能的亚细胞位置的正确预测率分别为96.1%和通过折刀测试为84.4%。通过自我一致性测试,真核蛋白的四种可能的亚细胞融合的正确预测率分别为95.6%和折刀测试为70.6%。

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