A comparison of categorisation algorithms for predicting the cellular localization sites of proteins

Abstract

A previous attempt to categorize yeast proteins based on certain attributes yielded only a 55% success rate of correct categorisation using a new type of decision procedure. This paper considers using existing soft computing approaches to improve the categorisation. More specifically, learning algorithms based on neural networks, growing cell systems, a rule development algorithm and genetic algorithms are applied to the yeast data. All of the results are at least as good as the original data showing that new problems do not necessarily require new algorithms. More interestingly as a consequence of using different algorithms, a consistent failure to achieve high success rates actually indicates features of the data rather than the failings of one or other of the algorithms.