Feasible prediction in S-system models of genetic networks

To construct the model of gene expression using microarray techniques can reveal the regulation rules from the gene expression profiles. From S-system model, it is able to analyze the regulatory system dynamics. However, with 2N(N + 1) parameters (called a set), an S-system model of N-gene genetic networks takes lots of iterations to have convergent gene expression profiles. To mining the association between the gene expression profiles and 2N(N + 1) parameters may provide information about the probability of the convergent gene expression profiles instead of trying to obtain the convergent gene expression profiles in lots of iteration. Based on this novel approach, higher accuracy of the binary classifier can be used to analyze and prediction the convergence of the gene expression profiles from an initial set to reduce the search time of the inference problem. This paper applies popular data mining algorithms to the classification tasks and compares their accuracy rates with a dataset (250 cases, including 176 training cases and 74 test cases). According to decision rules of the chosen classifier, we can provide a convergence prediction of time-series gene expression profiles on the given set of parameters.