CAJ | 학술논문

模体发现问题是分析基因转录调控关系的一个重要方面.提出了一种新的基于熵的聚类求精算法———ECRmotif,用于DNA序列中的模体发现问题.ECRmotif使用灵活的概率模型从背景序列中鉴别模体.它首先使用一个基于熵的聚类过程将数据集划分为若干子集,并对各候选子集压缩其实例的搜索空间,求精得到模体.通过模拟数据和真实数据的实验,表明 ECRmotif 算法可以有效地提高运行速度和效率,并准确地找出模体.
모체발현문제시분석기인전록조공관계적일개중요방면.제출료일충신적기우적적취류구정산법———ECRmotif,용우DNA서렬중적모체발현문제.ECRmotif사용령활적개솔모형종배경서렬중감별모체.타수선사용일개기우적적취류과정장수거집화분위약간자집,병대각후선자집압축기실례적수색공간,구정득도모체.통과모의수거화진실수거적실험,표명 ECRmotif 산법가이유효지제고운행속도화효솔,병준학지조출모체.
The motif discovery problem is an important aspect of the analysis of gene transcriptional regulatory relationship. This paper describes a novel entropy-based cluster refinement algorithm (ECRmotif) for motif discovery in DNA sequences.ECRmotif employs a flexible probabilistic model to identify motif from the background sequences.It first utilizes an entropy-based cluster process to divide the dataset into several subsets,and then reduces the instances searching space for each candidate subset and refines the motif from the candidate subsets. Experiments by using both synthetic and real datasets demonstrate that our algorithm increases the running speed and efficiency and finds motif accurately.