A Second-Order Learning Algorithm for Computing Optimal Regulatory Pathways

Abstract

Gene regulatory pathways play an important role in the functional understanding and interpretation of gene function. Many different approaches have been developed to model and simulate gene regulatory networks. In this paper we present the results of an iterative new second-order learning algorithm based on the multilayer perceptron (MLP) for generating optimal gene regulatory pathways by using ordinary differential equations. The algorithm based on Newton’s method is independent on the learning parameter and overcomes the drawbacks of the standard backpropagation (BP) algorithm. The methodology generates flow vectors which indicate the flow of mRNA and thereby the protein produced from one gene to another gene. A set of weighting coefficients representing concentration of various transcription factors is incorporated. The gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. Two gene regulatory networks are used to demonstrate the efficiency of the proposed learning algorithm. A comparative study with the existing extreme pathway analysis (EPA) also forms a part of this study. Results reported in the paper were corroborated by the same reported in the literature.