Classification of protein localisation patterns via supervised neural network learning

There are so many existing classification methods from diverse fields including statistics, machine learning and pattern recognition. New methods have been invented constantly that claim superior performance over classical methods. It has become increasingly difficult for practitioners to choose the right kind of the methods for their applications. So this paper is not about the suggestion of another classification algorithm, but rather about conveying the message that some existing algorithms, if properly used, can lead to better solutions to some of the challenging real-world problems. This paper will look at some important problems in bioinformatics for which the best solutions were known and shows that improvement over those solutions can be achieved with a form of feed-forward neural networks by applying more advanced schemes for network supervised learning. The results are evaluated against those from other commonly used classifiers, such as the K nearest neighbours using cross validation, and their statistical significance is assessed using the nonparametric Wilcoxon test.