Combining Classifiers for Protein Secondary Structure Prediction - Aydin Z.; Uzut, ÖMMU GÜLSÜM (Institute of Electrical and Electronics Engineers Inc., 2018)
Protein secondary structure prediction is an important step in estimating the three dimensional structure of proteins. Among the many methods developed for predicting structural properties of proteins, hybrid classifiers and ensembles that combine predictions from several models are shown to improve the accuracy rates. In this paper, we train, optimize and combine a support vector machine, a deep convolutional neural field and a random forest in the second stage of a hybrid classifier for protein secondary structure prediction. We demonstrate that the overall accuracy of the proposed ensemble is comparable to the success rates of the state-of-the-art methods in the most difficult prediction setting and combining the selected models have the potential to further improve the accuracy of the base learners. © 2017 IEEE.
9th International Conference on Computational Intelligence and Communication Networks, CICN 2017 -- 16 September 2017 through 17 September 2017 -- -- 135332
Keywords: bioinformatics; deep learning; ensemble methods; hybrid classifiers; protein secondary structure prediction