Annealing Robust Fuzzy Basis Function for Modeling of Molecular Autoregulatory Feedback Loop Systems with Outliers

Bioinformatics is the computing response to the molecular revolution in biology. This revolution has reshaped the lift sciences and given us a deep understanding of DNA sequences, RNA synthesis and the generation of proteins. This process can be represented as gene expression of molecular autoregulatory feedback loop systems. In this paper, the annealing robust fuzzy basis function (ARFBF) is proposed to improve the problems of fuzzy basis function for modeling of gene expression of molecular autoregulatory feedback loop systems with outliers. Firstly, the support vector regression (SVR) approach is proposed to determine the initial structure of ARFBF. Because of a SVR approach is equivalent to solving a linear constrained quadratic programming problem under a fixed structure of SVR, the number of hidden nodes, the initial parameters and the initial weights of ARFBF are easy obtained via the SVR approach. Secondly, the results of SVR are used as initial structure in ARFBF. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for ARFBF, and applied to adjust the parameters as well as weights of ARFBF. That is, an ARLA is proposed to overcome the problems of initialization and the cut-off points in the robust learning algorithm. Hence, when an initial structure of ARFBF is determined by a SVR approach, the ARFBF with ARLA have fast convergence speed and robust against outliers for the modeling of molecular autoregulatory feedback loop systems.