【摘要】 提出一种数据挖掘方法 MMHC来求解DNA序列模体。首先使用基于种子的错配聚类形成候选模体类,然后使用基于相对熵及聚类复杂度的深度优先判定(depth first determination,DFD)算法识别真正的模体类,最后使用保守区扫描法(conservation region scanning,CRS)及最大后验概率保值过滤法(MAP value-preservation filtering,MVPF)优化模体类。在两类DNA序列数据集上,将MMHC与三种经典的模体发现方法 MEME、AlignACE和SOMBRERO进行了对比试验。结果表明:对于大多数数据集,MMHC方法无论是在发现模体的可靠性及准确性方面,还是在反映背景种类的聚类结构方面,都明显优于三种经典的模体发现方法。更多还原

【Abstract】 A data mining method MMHC was given to solve DNA sequences motifs.The seed-based mismatch clustering was used to form the candidate motif clusters.Then the depth first determination(DFD) algorithm based on relative entropy and cluster complexity was proposed to identify the true motif clusters.Finally,the conservation region scanning(CRS) and MAP value-preservation filtering(MVPF) were given to optimize motif clusters.The experiment was conducted by testing MMHC method and comparing its performance with other three classic motif discovery methods MEME,AlignACE and SOMBRERO on two classes of DNA sequences datasets.Experimental results show the superiority of MMHC method over the three classic motif discovery methods in reliability,precision and the reflection of the cluster structure of the background species for most of the DNA sequences datasets.更多还原