WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 13Citation - Scopus: 20IGPRED: Combination of Convolutional Neural and Graph Convolutional Networks for Protein Secondary Structure Prediction(Wiley, 2021-05-25) Gormez, Yasin; Sabzekar, Mostafa; Aydin, ZaferThere is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.Conference Object Citation - WoS: 3Citation - Scopus: 2Combining Classifiers for Protein Secondary Structure Prediction(IEEE, 2017) Aydin, Zafer; Uzut, Ommu GulsumProtein 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.