计算机科学 ›› 2011, Vol. 38 ›› Issue (10): 169-173.

• 数据库与数据挖掘 • 上一篇    下一篇

基于混合SVM方法的蛋白质二级结构预测算法

隋海峰,曲武.钱文彬,杨炳儒   

  1. (北京科技大学信息工程学院 北京100083)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Protein Secondary Structure Prediction Algorithm Based on Mixed-SVM Method

SUI Hai-feng,QU Wu,QIAN Wen-bin ,YANG Bing-ru   

  • Online:2018-11-16 Published:2018-11-16

摘要: 预测蛋白质二级结构,是当今生物信息学中一个难以解决的问题。由于预测蛋白质二级结构的精度在蛋白 质结构研究中起到非常重要的作用,因此在基于KDTICM理论基础上,提出一种基于混合SVM方法的蛋白质二级 结构预测算法。该算法有效地利用蛋白质的物化属性和PSI-SEARCH生成的位置特异性打分矩阵作为双层SVM的 输入,从而大大地提高了蛋白质二级结构预测的精度。实验比较分析表明,新算法的预测精度和普适性明显优于目前 其他典型的预测方法。

关键词: 蛋白质二级结构预测,混合SVM方法,复合金字塔模型

Abstract: Protein secondary structure prediction is one of the most important problems in bioinformatics. I}he protein secondary structure prediction accuracy plays an important role in the field of protein structure research. In this paper, using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) , an efficient protein seconda- ry structure prediction algorithm based on mixed-SVM ( support vector machine) approach was proposed. The algo- rithm makes full use of the evolutionary information contained in the physicochemical properties of each amino acid and a position- specific scoring matrix generated by a PSI-SEARCH multiple sequence alignment, secondary structure can be predicted at significantly increased accuracy. At last, the experiments were used to show the superior accuracy and gen- erality of the new algorithm than other classical algorithm.

Key words: Protein secondary structure prediction, Mixed-SVM method, Compound pyramid model

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