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SummaryFunctional annotation of a protein sequence in the absence of experimental data or clear similarity to a sequence of known function is difficult. In this study, a simple set of sequence attributes based on physicochemical and predicted structural characteristics were used as input to machine learning methods. In order to improve performance through increasing the data available for training, a technique of sequence enrichment was explored. These methods were used to predict membership todoi:10.1515/jib-2009-109 fatcat:j4vjketebjhw7le3w34ta2den4