Incremental Learning for Classification of Protein Sequences

The problem of protein structural family classification remains a core problem in computational biology, with application of this technology applicable to problems in drug discovery programs and hypothetical protein annotation. Many machine learning tools have been applied to this problem using static machine learning structures such as neural networks or support vector machines that are unable to accommodate new information into their existing models. We utilize the fuzzy ARTMAP as an alternate machine learning system that has the ability of incrementally learning new data as it becomes available. The fuzzy ARTMAP is found to be comparable to many of the widespread machine learning systems. The use of an evolutionary strategy in the selection and combination of individual classifiers into an ensemble system, coupled with the incremental learning ability of the fuzzy ARTMAP is proven to be suitable as a pattern classifier. The algorithm presented is tested using data from the G-Coupled Protein Receptors Database and shows good accuracy of 83%.

I. Introduction

Protein sequence analysis has become important area of research due to its application in drug discovery programs [1] with computational analysis becoming popular. Consider the problem of new drug development, which often takes up to 15 years and costing up to $700 million per drug under investigation [1]. Computational toolshave had the most impact in the discovery phase of drug design. In pharmaceutical drug discovery programs it is often useful to classify the sequences of proteins into a number of known families. In a mathematical notation, if it is known that a sequence is obtained for some disease , and that belongs to family , treatment for the disease is initially determined using a combination of drugs that are known to apply to [2].