Browsing by Author "Kaynar, Oguz"

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Citation - WoS: 6
Citation - Scopus: 7
Dimensionality Reduction for Protein Secondary Structure and Solvent Accesibility Prediction
(World Scientific Publ Co Pte Ltd, 2018) Aydin, Zafer; Kaynar, Oguz; Gormez, Yasin
Secondary structure and solvent accessibility prediction provide valuable information for estimating the three dimensional structure of a protein. As new feature extraction methods are developed the dimensionality of the input feature space increases steadily. Reducing the number of dimensions provides several advantages such as faster model training, faster prediction and noise elimination. In this work, several dimensionality reduction techniques have been employed including various feature selection methods, autoencoders and PCA for protein secondary structure and solvent accessibility prediction. The reduced feature set is used to train a support vector machine at the second stage of a hybrid classifier. Cross-validation experiments on two difficult benchmarks demonstrate that the dimension of the input space can be reduced substantially while maintaining the prediction accuracy. This will enable the incorporation of additional informative features derived for predicting the structural properties of proteins without reducing the accuracy due to overfitting.
Citation - WoS: 2
Citation - Scopus: 1
Feature Selection for Protein Dihedral Angle Prediction
(IEEE, 2017) Aydin, Zafer; Kaynar, Oguz; Gormez, Yasin
Three-dimensional structure prediction has crucial importance for bioinformatics and theoretical chemistry. One of the main steps of three-dimensional structure prediction is dihedral (torsion) angle prediction. As new feature extraction methods are developed the dimension of the input space increases considerably yielding longer model training and less accurate models due to noisy or redundant features. In this study, feature selection is employed for dimensionality reduction on one of the established benchmarks of protein 1D structure prediction. Experimental results show that the feature selection improves the accuracy of protein dihedral angle class prediction by 2% and can eliminate up to %82 of the features when random forest classifier is used. Accurate prediction of dihedral angles will eventually contribute to protein structure prediction.