Using genetic algorithm combining adaptive neuro-fuzzy inference system and fuzzy differential equation to optimizing gene

Abstract

Gene optimization is a popular problem to research both in experiment and in analyzing data Today there are many methods and models applied to this problem but some characteristic patterns in data which have not been learnt such as missing data Moreover missing data contains lack of information in parameters of differential equation so some differential equations of biological system cannot be computed The aim of this study is to evaluate and learn from missing information in data as well as in solving differential equation We have two different models for two problems Adaptive Neuro fuzzy inference system ANFIS dealt with missing information in data Fuzzy differential equations FDEs are used to model and compute differential equation when it missed information in the equation Overall the results in ANFIS model are larger than both in testing and in training Besides that the statistical testing in FDEs model has not a good performance in using to predict gene expression with data However we propose a new process to use fuzzy methods to solve differential equation and train data when those are missing some essential values