A Model Based Approach On Gene Expression Profiling Of Colorectal Cancer And Normal Mucosa Using Logistic Regression, Artificial Neural Network And Structural Equation Modelling
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Abstract
Colorectal cancer is one of the leading biomedical issues of concern. Our understanding of the disease has improved using microarray expression data analysis over last few decades. Therefore, it is of interest to design conceptual statistical models to analyze volumes of data to glean useful information. We used data from the open source Gene Expression Omnibus database containing information on160 normal mucosa tissues and 203 CRCs. The model described in this report selected 44 candidate genes exceeding 4-fold threshold expression. The reliability was determined using Cronbach’s alpha measurement for further statistical analysis. Structural Equation Modelling and binary logistic regression and neural networks were used to incur the strength of association of genes with the disease outcome.
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