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A fundamental problem in hu man health is to predict the effect of genes that cause disease; this is an important step to diagnosis and treatment. Also predict ion gene functions are still a challenge for bio logists in the post-genomic era. DNA microarray simu ltaneously monitors expression levels thousands of genes; the massive gene expression data provide us with unique opportunities to analyze the functional and regulatory relat ionships among genes. In this paper, new approach to estimate the relationship among genes and the effects of each gene on diseases is proposed. This approach consists of four main steps: First, extract the subset of high informative genes. Second, constructing genes network; thus, we propose to predict a gene's functions according to its context graph, wh ich is defined as the gene interaction network co mposed of the genes interacting directly and indirectly based on focal gene. Third, path analysis model is used to estimate the effect of genes on the disease and among each other. Fourth, obtains the relation among genes and the target (disease) by applying logistic regression analysis for predict new samples. This approach evaluated by lung cancer Microarray dataset. The proposed path diagram fit the subset of top ranked genes expression dataset, the Goodness of Fit Index (GFI) was > 0.832. The value of this approach is that it not only tackles the measurement problem by path analysis but also provides a visualization o f the relat ionship among genes. The proposed approach also is useful for feature reduction, due to evaluate all genes simultaneously vs. lung state. |
