Applying the Fisher score to identify Alzheimer's disease-related genes
Biologists and scientists can use the data from Alzheimer's disease (AD) gene expression microarrays to mine AD disease-related genes. Because of disadvantages such as small sample sizes, high dimensionality, and a high level of noise, it is difficult to obtain accurate and meaningful biological information from gene expression profiles. In this paper, we present a novel approach for utilizing AD microarray data to identify the morbigenous genes. The Fisher score, a classical feature selection method, is utilized to evaluate the importance of each gene.