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。
Protein secondary structure prediction is a fundamental and important component in the study of protein structure and functions.
蛋白质二级结构是研究蛋白质结构和功能的基础和重要组成部分。
Protein secondary structure prediction becomes the most important step of predicting the space conformation from protein molecule.
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。
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.
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。
To improve the prediction results of protein secondary structure, we developed a neural network ensemble model based on dual-layer feed forward BP network.
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.
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。
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