免疫聚类算法在基因表达数据分析中的应用

Application of Immune Clustering Algorithm to the Analysis of Gene Expression Data

提出了基于免疫聚类算法的基因表达数据分析方法.根据基因表达数据矩阵的特点,设计了改进的Consine系数来度量基因相似度;借鉴生物免疫学的有关免疫理论,利用基因表达数据分析的先验知识自适应地改变抗体本身及其与抗原亲合度的关系,构造了基于免疫优势克隆的聚类算法.与K-均值算法和遗传算法的对比实验表明,该算法能够获得较大的类内紧制度、较小的类间分离度,具有较好的工程应用价值.

An analysis method of gene expression data based on immnue clustering algorithm is presented.A modified consine coefficient is put forward to measure comparability of genes in accordance with the characteristic of gene expression data matrix.Inspired by the biology immune system,a new clustering algorithm based on immunodominance cloning（ICCA）is designed.In comparison with K-means algorithm and genetic K-means algorithm,the proposed ICCA given can achieve good class compactness and separability.