Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
5 results
Search Results
Conference Object Citation - Scopus: 1Feature Selection for Protein Dihedral Angle Prediction(Institute of Electrical and Electronics Engineers Inc., 2017-09) Aydin, Zafer; Kaynar, Oǧuz; Görmez, YasinArticle 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, MostafaConference Object Citation - Scopus: 1The Identification of Discriminative Single Nucleotide Polymorphism Sets for the Classification of Behçet's Disease(Institute of Electrical and Electronics Engineers Inc., 2018-09) Görmez, Yasin; Işik, Yunus Emre; Bakir-Güngör, BurcuBehçet's disease is a long-term multisystem inflammatory disorder, characterized by recurrent attacks affecting several organs. As the genotyping individuals get cheaper and easier following the developments in genomic technologies, genome-wide association studies (GWAS) emerged. By this means, via studying big-sized case-control groups for a specific disease, potential genetic variations, single nucleotide polymorphisms (SNPs) are identified. Although several genetic risk factors are identified for Behçet's disease with the help of these studies via scanning around a million of SNPs, these variations could only explain up to 20% of the disease's genetic risk. In this study, for Behçet's disease classification, via comparing all the SNPs genotyped in GWAS, with the SNPs selected via using genetic knowledge, gain ratio and information gain; both reduction in the feature size and improvement in the classification accuracy is aimed. Also, using different classification algorithms such as random forest, k-nearest neighbour and logistic regression, their effects on the classification accuracy are investigated. Our results showed that compared to other feature selection methods, with at least 81% success rate, the selection of the SNPs using the genetic information (of their GWAS p-values, indicating the significance of the SNP against the disease) provides 15% to 42% improvement in all classification algorithms. This improvement is statistically sound. While gain ratio and information gain feature selection techniques yield similar classification accuracies, the models using all SNPs could not exceed 50% accuracies and results in the worst performance. © 2019 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 3ROSE: A Novel Approach for Protein Secondary Structure Prediction(Springer Science and Business Media Deutschland GmbH, 2021) Görmez, Yasin; Aydin, ZaferThree-dimensional structure of protein gives important information about protein’s function. Since it is time-consuming and costly to find the structure of protein by experimental methods, estimation of three-dimensional structures of proteins through computational methods has been an efficient alternative. One of the most important steps for the 3-D protein structure prediction is protein secondary structure prediction. Proteins which contain different number and sequences of amino acids may have similar structures. Thus, extracting appropriate input features has crucial importance for secondary structure prediction. In this study, a novel model, ROSE, is proposed for secondary structure prediction that obtains probability distributions as a feature vector by using two position specific scoring matrices obtained by PSIBLAST and HHblits. ROSE is a two-stage hybrid classifier that uses a one-dimensional bi-directional recurrent neural network at the first stage and a support vector machine at the second stage. It is also combined with DSPRED method, which employs dynamic Bayesian networks and a support vector machine. ROSE obtained comparable results to DSPRED in cross-validation experiments performed on a difficult benchmark and can be used as an alternative to protein secondary structure prediction. © 2021 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 3Citation - Scopus: 13NSEM: Duygu Analizi için Özgün Yıǧınlanmiş Topluluk Yöntemi(Institute of Electrical and Electronics Engineers Inc., 2018-09) Işik, Yunus Emre; Görmez, Yasin; Kaynar, Oǧuz; Aydin, Zafer; Emre Isik, YunusToday, people often share their ideas, opinions and feelings through forums, social media sites, blogs and similar platforms. For this reason, access to these data has become very easy. Increase in the number of shares makes it possible to analyze and use these data in terms of marketing and politics. However, due to the large number of data, it is impossible that this analysis will be done by humans. Determination of what type of emotion is included automatically is done by sentiment analysis methods. In these methods, the text is defined as a mathematical vector and classified by machine learning methods. Ensemble methods are one of the most important methods used as classifiers in sentiment analysis. In these methods, a classifier error is tried to be solved by another classifier. In sentiment analysis, the feature vector that describes the text is as important as the classifier. Feature vectors obtained using different methods can make mistakes in different places. For this reason, in this study, NSEM is proposed for sentiment analysis, which is a new ensemble method that uses 2 different classifiers and 2 different feature extraction methods. As a result of the analysis, the proposed method is the most successful method with an accuracy rate of 79.1%. © 2019 Elsevier B.V., All rights reserved.