Feature identification and reduction for improved generalization accuracy in secondary-structure prediction

Secondary structure prediction is an important step in understanding gene function. Several algorithms have been proposed for applying machine learning techniques to this problem. This research examines these algorithms and constructs a framework that is effective in providing accurate predictions.

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