Probability Based Most Informative Gene Selection From Microarray Data

摘要

>Microarray datasets have a wide application in bioinformatics research. Analysis to measure the expression level of thousands of genes of this kind of high-throughput data can help for finding the cause and subsequent treatment of any disease. There are many techniques in gene analysis to extract biologically relevant information from inconsistent and ambiguous data. In this paper, the concepts of functional dependency and closure of an attribute of database technology are used for finding the most important set of genes for cancer detection. Firstly, the method computes similarity factor between each pair of genes. Based on the similarity factors a set of gene dependency is formed from which closure set is obtained. Subsequently, conditional probability based interestingness measurements are used to determine the most informative gene for disease classification. The proposed method is applied on some publicly available cancerous gene expression dataset. The result shows the effectiveness and robustness of the algorithm.