Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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.Article Citation - WoS: 5Citation - Scopus: 5Novel Antimicrobial Peptide Design Using Motif Match Score Representation(IEEE Computer Soc, 2024-11) Soylemez, Ummu Gulsum; Yousef, Malik; Kesmen, Zulal; Bakir-Gungor, BurcuAntimicrobial peptides (AMPs) have drawn the interest of the researchers since they offer an alternative to the traditional antibiotics in the fight against antibiotic resistance and they exhibit additional pharmaceutically significant properties. Recently, computational approaches attemp to reveal how antibacterial activity is determined from a machine learning perspective and they aim to search and find the biological cues or characteristics that control antimicrobial activity via incorporating motif match scores. This study is dedicated to the development of a machine learning framework aimed at devising novel antimicrobial peptide (AMP) sequences potentially effective against Gram-positive/Gram-negative bacteria. In order to design newly generated sequences classified as either AMP or non-AMP, various classification models were trained. These novel sequences underwent validation utilizing the "DBAASP: strain-specific antibacterial prediction based on machine learning approaches and data on AMP sequences" tool. The findings presented herein represent a significant stride in this computational research, streamlining the process of AMP creation or modification within wet lab environments.Conference Object Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets(Springer Verlag service@springer.de, 2018) Cetiner, Erkan; Güngör, Vehbi Çağrı; Kocak, TaskinHybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches. © 2018 Elsevier B.V., All rights reserved.Article Citation - WoS: 11Citation - Scopus: 14Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform(World Scientific Publ Co Pte Ltd, 2021) Karakullukcu, Nedime; Yilmaz, BulentPatients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, p, less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.Conference Object Citation - Scopus: 5Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model(Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, BulentEmotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.