基于蛋白质二级序列的关联多分类算法

蛋白质二级结构预测是公认的生物信息学领域的国际性难题。以基于内在认知机理的知识发现理论(knowledge discovery theory based on inner cognitive mechanism, KDTICM)理论的扩展性研究与数据库中的知识发现（knowledge discovery in database*, KDD*)模型为基础,提出一种基于结构序列的多分类算法--SAC(structural association classification),可以有效地解决蛋白质二级结构预测问题。该算法借助设定支持度阈值的精化知识库的方法,其预测准确率能够超过85%。以该算法为核心,构建了一个蛋白质二级预测模型--复合金字塔模型。实验证明,在RS126、CB513、ILP数据集上的预测准确率均超过80%,超过目前已知的国际主流水平。

Association multi-classification algorithm based on protein secondary structure sequence

The prediction of protein secondary structure is one of the major issues in Bioinformatics. As one of the researches of KDTICM theory, a multi classification algorithm based on structure sequence is proposed, which is based on knowledge discovery in database* (KDD*) model. The SAC algorithm can effectively solve the problem of protein secondary structure prediction. The algorithm’s accuracy exceeded by 85% by using the reduction of knowledge base through the setting of the confidence threshold value. A compound pyramid model is built with the SAC algorithm being regarded as a kernel. Experimental results show that the predictive accuracy exceeded by 80% when using in the datasets of RS126,CB513 and ILP, which is equivalent or even excels known national and international levels.