Analysis and construction of genetic network for mice brain microarray datasets

This paper intends to find out target genes about memory and learning via microarray analysis. Microarrays are often used to store and manage large amounts of data; however, there is no consensus as to how to best analyze microarray data. This paper uses computational algorithms to analyze gene samples from mice with various calcium channel phenotypes. Min-max normalization was used first to normalize the data. Then, analysis of variance was applied to detect genetic differences among the genes. Finally, Pearson correlation coefficients were calculated to identify the regulatory network of the genes. This analysis model can be applied to efficiently analyze complicated gene expression data. It can also be used to examine the biological functions and regulations of target genes.