Using Probabilistic Neural Networks to Predict the Localization of Proteins in Cell Compartments

Abstract

This paper describes the use of probabilistic neural networks to solve problems of bioinformatics by the example of determining the localization of proteins according to their primary structure. The data used are sets of characteristics of amino acid sequences of proteins, obtained by various software tools aimed at finding specific signal sequences, as well as data on where these proteins are localized in cells of two microorganisms — E. coli and S. cerevisiae. The data source is the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets). The possibility of using probabilistic neural networks to solve this problem is shown, since the classification accuracy of 57.5% and 85.0% for yeast and for bacterial cells is obtained, respectively. The obtained indicators of the accuracy of the classification of the data used exceed those that, according to the literature, were achieved with the use of other recognition methods. It is noted that a high learning rate and the possibility of modification make probabilistic neural networks a promising tool for analyzing bioinformatics data. In silico