Combining Classifiers for Protein Secondary Structure Prediction


AYDIN Z. , Uzut O. G.

9th International Conference on Computational Intelligence and Communication Networks (CICN), Girne, Kıbrıs (Kktc), 16 - 17 Eylül 2017, ss.29-33 identifier

  • Doi Numarası: 10.1109/cicn.2017.9
  • Basıldığı Şehir: Girne
  • Basıldığı Ülke: Kıbrıs (Kktc)
  • Sayfa Sayıları: ss.29-33

Özet

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.