首页> 外文会议>Advances in Neural Networks - ISNN 2007 pt.2; Lecture Notes in Computer Science; 4492 >Prediction of Helix, Strand Segments from Primary Protein Sequences by a Set of Neural Networks
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Prediction of Helix, Strand Segments from Primary Protein Sequences by a Set of Neural Networks

机译:通过一组神经网络从一级蛋白质序列预测螺旋,链段

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摘要

In prediction of secondary structure of proteins there are always some suspected segments. These suspected segments confuse people and lower the accuracy of prediction methods. To deal with this problem, a set of neural networks (NNs) are built based on helix, strand and coil segments selected from PDB. The test performance of these NNs on training data is perfect without surprise. However the prediction on test data is not good enough because the training data are lake of great representativeness. The results support the fact that closer neighbor vectors have the similar outputs of NNs. One can improve representativeness of training data without enlarging data scale as long as select less data from dense region and more from sparse region on condition that distribution of sample data has been known.
机译:在预测蛋白质的二级结构时,总会有一些可疑的片段。这些可疑的细分会使人们感到困惑,并降低了预测方法的准确性。为了解决这个问题,基于选自PDB的螺旋,链和线圈段构建了一组神经网络(NN)。这些NN在训练数据上的测试性能完美无缺。但是,由于训练数据具有很高的代表性,因此对测试数据的预测还不够好。结果支持以下事实:较近的邻居向量具有类似的NN输出。只要在已知样本数据分布的情况下,从密集区域中选择较少的数据,而从稀疏区域中选择更多的数据,则可以在不扩大数据规模的情况下提高训练数据的代表性。

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