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Motivation: A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using Support Vector Machines (SVMs). Results: Three different sets of features baseddoi:10.1093/bioinformatics/btl453 pmid:16940325 fatcat:nvhajw7pife7nkl5snl3eoe2fa