摘要
随着基因芯片技术的快速发展以及其在基因表达分析等过程中的应用,产生了大量的基因表达谱数据,如何处理和分析这些数据并从中提取出有价值的生物学信息成为一个极为重要的课题,基因分类是进行基因数据处理的常用方法。本文首先利用主成分分析法(PCA)把基因的多个属性转化为少数几个综合属性,将基因表达谱数据映射成一个带权图,并将图论的最小生成树理论引入基因分类分析方法,然后设计了基于最小生成树的基因分类算法,理论分析和仿真结果表明了该算法的可行性和有效性。
With the high speed of the development of microarray technology, the application of it cause huge volumes of gene expression profile data. It is important how to handle and analyze these data and to extract some valuable information from them. The gene classification is a common method for gene expression data processing. In this paper, firstly multi-properties of gene change to several principal component by the Principal Component Analysis (PCA), and the gene expression profile data that is changed to network, the Minimum Spanning Tree, a graph-theoretic approach, are dealt with; then gene classification algorithm based Minimum Spanning Trees is designed. The theoretic analysis and the simulation results suggest that the scheme is feasible and effective.
出处
《湖南人文科技学院学报》
2004年第6期131-133,共3页
Journal of Hunan University of Humanities,Science and Technology
