摘要
本文主要研究了Motif预测算法,在贪心EM预测算法基础上进行分析优化,并形成新的预测方法。工作重点是在参数的初始化,对参数模型的重新划分并引入Kd-树的层次聚类的方法,建立新的PKG算法。预测结果表明,在预测较大数据集方面新算法有一定的优势,尤其是对同一物种的序列预测具有更强的搜索和分类能力,在没有影响时间复杂度的前提下显著的提高了搜索的效率。
Motif finding algorithm was studied as the key point in this paper.Optimization was based on the EM-baced greedy algorithm and then predicting method was established.Parameters initialization,the re-division of the parameter model and introduction of Kd-tree hierarchical clustering method,and the establishment of PKG algorithm were paid more attention in this paper.The results indicated PKG algorithm has some advantages in predicting motifs in large data sets,especially in the prediction of sequences in the same species.With advantages in sequence search and classification capabilities,the search efficiency was improved by PKG algorithm significantly without affecting complexity in the time.
出处
《价值工程》
2011年第17期141-142,共2页
Value Engineering
基金
国家自然科学基金资助项目(30600329)
