Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article GraphUnet-SS: A Novel Deep Learning Model for Protein Secondary Structure Prediction Based on U-Net Architecture(Elsevier Ltd, 2026-04) Aydin, Zafer; Görmez, Yasin; Sabzekar, MostafaArticle Citation - WoS: 13Citation - Scopus: 21IGPRED: 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.Article Citation - WoS: 10Citation - Scopus: 13A Noise-Aware Feature Selection Approach for Classification(Springer, 2021-02-17) Sabzekar, Mostafa; Aydin, ZaferA noise-aware version of support vector machines is utilized for feature selection in this paper. Combining this method and sequential backward search (SBS), a new algorithm for removing irrelevant features is proposed. Although feature selection methods in the literature which utilize support vector machines have provided acceptable results, noisy samples and outliers may affect the performance of SVM and feature selections method, consequently. Recently, we have proposed relaxed constrains SVM (RSVM) which handles noisy data and outliers. Each training sample in RSVM is associated with a degree of importance utilizing the fuzzy c-means clustering method. Therefore, a less importance degree is assigned to noisy data and outliers. Moreover, RSVM has more relaxed constraints that can reduce the effect of noisy samples. Feature selection increases the accuracy of different machine learning applications by eliminating noisy and irrelevant features. In the proposed RSVM-SBS feature selection algorithm, noisy data have small effect on eliminating irrelevant features. Experimental results using real-world data verify that RSVM-SBS has better results in comparison with other feature selection approaches utilizing support vector machines.