基于FCM算法的小脑基因模糊聚类分析

On the Fuzzy Clustering of the Cerebellar gene Based on FCM Algorithm

探讨基因表达数据的聚类分析方法,结合一种聚类结果的评判准则,应用于胎儿小脑基因表达数据,得到了最优的聚类结果,并做出了生物学解释.利用Matlab软件进行了仿真,利用模糊聚类Xie-Beni指数得到了最优聚类数,并把每一类对应的基因标号输出到txt文件,最后进行生物学解释.得到的小脑基因最优聚类数为3类,与生物学意义比较吻合,各类中的基因功能接近.基于FCM算法的基因模糊聚类是有效的,结果具有一定生物学意义,能对生物学基因聚类有一定指导作用.

To explore gene expression data of the cluster analysis method, combined with the results of a cluster evaluation criterion applied to fetal cerebellar gene expression data, have been the best clustering results, and made the biological interpretation. The use of Matlab software simulation, the use of fuzzy clustering Xie-Beni index has been the optimal cluster number, and the corresponding gene in each category label output to txt file, and finally to carry out the biological interpretation. To be the best of cerebellar gene cluster for the 3 categories, and comparison with biological significance, various types of gene function in the close. FCM algorithm based on fuzzy clustering of the gene to be effective, the results of a certain biological significance of gene clustering on the biological role of some guidance.