低同源蛋白质的二级结构预测至今仍然是一个困难的问题。
Prediction of lowly homological protein secondary structure is still a difficult problem up to now.
文章针对蛋白质二级结构预测这一复杂非线性模式分类问题，提出了基于径向基函数的预测方法。
Aiming at solving the complicated non-linear pattern classification problem of protein secondary structure prediction, a new method based on radial basis function is proposed.
本文中，我们利用数据挖掘得到的统计信息数据库对蛋白质的二级结构进行了预测。
However this process is quite tough. Here our statistical information database by data mining is used for predicting protein secondary structure.
为了提高蛋白质二级结构预测精度，本文尝试采用一种基于串联BP网络集成的二级结构预测模型。
To improve the prediction results of protein secondary structure, we developed a neural network ensemble model based on dual-layer feed forward BP network.
论文的另一个研究主题是蛋白质二级结构的预测方法。
Another research subject is the prediction of protein secondary structure.
蛋白质二级结构预测是蛋白质结构预测的重要组成部分，是蛋白质结构预测最关键的步骤。
Protein secondary structure prediction becomes the most important step of predicting the space conformation from protein molecule.
本文主要论述了神经网络在预测蛋白质二级结构方面的进展以及存在的问题。
Progress and problems in the field of predicting protein secondary structure by neural networks are presented in this paper.
表明： 基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。
It is shown that the BP neural network method combined with the amino-acid composition and the biased auto-covariance function features could effectively improve the prediction accuracy.
摘要：介绍了构造性机器学习方法——覆盖算法在蛋白质二级结构预测中的应用。
Absrtact: Mainly introduces protein secondary structure prediction based on structural machine learning-covering algorithm.
摘要：介绍了构造性机器学习方法——覆盖算法在蛋白质二级结构预测中的应用。
Absrtact: Mainly introduces protein secondary structure prediction based on structural machine learning-covering algorithm.
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