Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2Combining Classifiers for Protein Secondary Structure Prediction(Institute of Electrical and Electronics Engineers Inc., 2017-09) Aydin, Zafer; Uzut, Ömmu GülsümArticle Citation - WoS: 6Citation - Scopus: 7The Determination of Distinctive Single Nucleotide Polymorphism Sets for the Diagnosis of Behcet's Disease(IEEE Computer Soc, 2022-05-01) Isik, Yunus Emre; Gormez, Yasin; Aydin, Zafer; Bakir-Gungor, BurcuBehcet's Disease (BD) is a multi-system inflammatory disorder in which the etiology remains unclear. The most probable hypothesis is that genetic tendency and environmental factors play roles in the development of BD. In order to find the essential reasons, genetic changes on thousands of genes should be analyzed. Besides, there is a need for extra analysis to find out which genetic factor affects the disease. Machine learning approaches have high potential for extracting the knowledge from genomics and selecting the representative Single Nucleotide Polymorphisms (SNPs) as the most effective features for the clinical diagnosis process. In this study, we have attempted to identify representative SNPs using feature selection methods, incorporating biological information and aimed to develop a machine-learning model for diagnosing Behcet's disease. By combining biological information and machine learning classifiers, up to 99.64 percent accuracy of disease prediction is achieved using only 13,611 out of 311,459 SNPs. In addition, we revealed the SNPs that are most distinctive by performing repeated feature selection in cross-validation experiments.Article Citation - WoS: 3Citation - Scopus: 6IGPRED-Multitask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility(IEEE Computer Soc, 2023-03-01) Gormez, Yasin; Aydin, ZaferProtein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model.Conference Object Citation - Scopus: 8Constructing Structural Profiles for Protein Torsion Angle Prediction(SciTePress, 2015) Aydin, Zafer; Baker, David A.; Noble, William StaffordStructural frequency profiles provide important constraints on structural aspects of a protein and is receiving a growing interest in the structure prediction community. In this paper, we introduce new techniques for scoring templates that are later combined to form structural profiles of 7-state torsion angles. By employing various parameters of target-template alignments we improve the quality and accuracy of structural profiles considerably. The most effective technique is the scaling of templates by integer powers of sequence identity score in which the power parameter is adjusted with respect to the similarity interval of the target. Incorporating other alignment scores as multiplicative factors further improves the accuracy of profiles. After analyzing the individual strengths of various structural profile methods, we combine them with ab-initio predictions of 7-state torsion angles by a linear committee approach. We show that incorporating template information improves the accuracy of ab-initio predictions significantly at all levels of target-template similarity even when templates are distant from the target. Template scaling methods developed in this work can be applied in many other prediction tasks and in more advanced methods designed for computing structural profiles. © 2020 Elsevier B.V., All rights reserved.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.