An error tolerant software equipment for human DNA characterization

The problem addressed in this paper is to define a learning algorithm for the prediction of splice site locations in human DNA in the presence of sequence annotation errors in the training data. To this aim we generalize a previous machine learning algorithm. Experimental results on a common dataset including errors show the algorithm outperforms its previous version, in particular in the complexity of the produced hypothesis.