Soft Computing Methods for Prediction of Replication Origins in Caudoviruses

Abstract

Prediction methods that can be reduced to learning of partially defined multiple-valued functions have become very popular. In this paper, we consider a prediction problem related to DNA replication, which is essential for the reproduction of many viruses. Procedures to find replication origins are important for controlling such viruses. This paper focuses on the order of caudovirales and proposes a new prediction approach based on least-squares support vector machine (LS-SVM) and a multilayer feedforward neural network with multi-valued neurons (MLMVN). The results suggest that this method will be a useful tool for the prediction of viral replication origins.