The Genetic Code as a Multiple-Valued Function and Its Implementation Using Multilayer Neural Network Based on Multi-Valued Neurons

Abstract

The genetic code is the four-letter nucleic acid code, and it is translated into a 20-letter amino acid code from proteins (each of 20 amino acids is coded by the triplet of four nucleic acids). Thus, it is possible to consider the genetic code as a partially defined multiple-valued function of a 20-valued logic. It is shown in the paper that a model of multiple-valued logic over the field of complex numbers is the most appropriate for the representation of the genetic code. Furthermore, consideration of the genetic code within this model makes it possible to learn it using a multilayer neural network based on multi-valued neurons (MLMVN). The functionality of MLMVN is higher than the ones of the traditional feedforward and kernel-based networks and its backpropagation learning algorithm is derivative-free. It is shown that the genetic code multiple-valued function can be easily trained by a significantly smaller MLMVN in comparison with a classical feedforward neural network.