Research On Gene Expression Data Analysis Method And Its Application

By using bioinformatics analysis methods on the tens of thousands of gene expression data produced by microarray technology, researchers can reveal the function of the gene and the interaction relationship between genes.The cluster analysis of gene expression data is a hotspot in the bioinformatics research field, in this paper, a cluster analysis of gene expression data based on BIRCH algorithm is proposed. And according to the application of gene expression data, this paper discusses how to correctly and reasonably select the balancing factor B, branching factor L and threshold T in detail.Combined with the practical necessity of gene expression data research,this research is from the perspective of bioinformatics researchers, and deeply rooted in the bioinformatics research field.Deficiencies of clustering algorithm on the gene expression are recognized during this process.Therefor with the partition to overall research method, a search algorithm based on trend match is proposed in this paper. By using this algorithm, we can search the similar gene expression data or gene expression data with same variation trend. Based on above theory, we developed Gesture, an interactive graphical user interface(GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. Starting from a familiar gene expression, this analytical toolkit can excavate its co-expression genes, suppressor genes and time-delay or shift genes, and finally build the local regulatory networks from these associated genes. Gesture can process gene expression data efficiently and practical, and extract meaningful biology information from a mass of gene expression data accurately and quickly.