IC-kmedoids:适用于RNA二级结构预测的聚类算法

IC-kmedoids:A Clustering Algorithm for RNA Secondary Structure Prediction

采用自由能方法预测RNA二级结构时,如何精确有效地从次优结构中筛选出真实的二级结构成为RNA结构预测中的关键。采用聚类技术对次优结构集合进行分析,可有效地提高预测结果的精度。本文利用RBP分数矩阵,提出一种基于增量中心候选集的改进k-medoids算法。它将随机选择初始中心并进行首次划分后以中心候选集逐一扩展的方式进行中心轮换,以降低算法的复杂度。实验表明,该算法能取得更高的CH值,且能有效地缩短计算时间。

Due to the minimum free energy model,it is very important to predict the RNA secondary structure accurately and efficiently from the suboptimal foldings.Using clustering techniques in analyzing the suboptimal structures could effectively improve the prediction accuracy.An improved k-medoids cluster method is proposed to make this a better accuracy with the RBP score and the incremental candidate set of medoids matrix in this paper.The algorithm optimizes initial medoids through an expanding medoids candidate sets gradually.The predicted results indicated this algorithm could get a higher value of CH and significantly shorten the time for calculating clustering RNA folding structures.