Aydin, ZaferUzut, Ommu Gulsum2021-08-022021-08-022017978-1-5090-5001-72375-8244https://doi.org/10.1109/CICN.2017.9https://hdl.handle.net/20.500.12573/890All computations were performed on TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA Resources). This work is supported by grant 113E550 from 3501 TUBITAK National Young Researchers Career Award.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.enginfo:eu-repo/semantics/closedAccessdeep learningensemble methodshybrid classifiersprotein secondary structure predictionbioinformaticsconferenceObject2933