基于混合SVM方法的蛋白质二级结构预测算法

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。

Protein Secondary Structure Prediction Algorithm Based on Mixed-SVM Method

Protein secondary structure prediction is one of the most important problems in bioinformatics. I}he proteinsecondary 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 anda position- specific scoring matrix generated by a PSI-SEARCH multiple sequence alignment, secondary structure can bepredicted 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.