Abstract
Protein fold recognition is an important approach to structure discovery without relying on sequence similarity. In this context, computer-based tools, mainly the techniques from machine learning (ML), have become essential considering the large volume of data. We present an empirical comparison of individual machine learning techniques (k-nearest neighbor, naive Bayes, decision trees, support vector machines and neural networks) and ensemble approaches (bagging and boosting) to the task of protein structural class prediction.
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