面向微阵列基因数据的基于PA指标双向聚类算法

A Biclustering Algorithm Based on PA Index in Microarray Gene Expression Data

针对目前双聚类算法很少考虑所得聚类结果整体的划分质量问题,提出一种基于PA指标的双聚类算法。该算法选定一种衡量所有簇划分效果的PA指标来构造双聚类的模型,运用启发式贪心策略,通过迭代增删行列的方式挖掘出划分效果较高的几个双聚类。将所提算法与CC、FLOC算法进行算法性能的比较。实验结果表明,该算法能获得更好的结果。这说明该算法更能挖掘出具备既有统计意义又有生物意义的局部模式。

To improve the global quality of the outcome of the biclustering , a biclustering algorithm based on PA index was pro-posed .In this algorithm , the PA index which can estimate the effect of outcome of the biclustering was chosen to construct the bi -cluster model.Through deleting or inserting rows and columns in the heuristic greedy fashion , the algorithm obtained the signifi-cant biclusters of high global quality .To compare the performance of the algorithm with CC and FLOC , a real dataset experiment was simulated.The result shows that the algorithm in this paper can obtain better results .In a word, the algorithm is capable of detecting potentially statistically and biologically significant biclusters .