An empirical comparison of individual machine learning techniques and ensemble approaches in protein structural class prediction

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.