Predicting protein secondary structure with a nearest-neighbor algorithm.
We have developed a new method for protein secondary structure prediction that achieves accuracies as high as 71.0%, the highest value yet reported. The main component of our method is a nearest-neighbor algorithm that uses a more sophisticated treatment of the feature space than standard nearest-neighbor methods. It calculates distance tables that allow it to produce real-valued distances between amino acid residues, and attaches weights to the instances to further modify the the structure of feature space. The algorithm, which is closely related to the memory-based reasoning method of Zhang et al., is simple and easy to train, and has also been applied with excellent results to the problem of identifying DNA promoter sequences.
Salzberg
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Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218.
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D000465:Algorithms; D000595:Amino Acid Sequence; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary