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    BOOSTING THE PERFORMANCE OF PSEUDO AMINO ACID COMPOSITION
    (AMER SOC MECHANICAL ENGINEERS, 2011) Goktepe, Yunus Emre; Ilhan, Ilhan; Kahramanli, Sirzat
    Protein-protein interactions are critical in coordinating various cellular processes. They help understanding protein function and drug design. Extracting protein features from amino acid sequences is important in order to study protein-protein interactions. Various feature extraction approaches for proteins have been introduced up to the present. PseAAC is one of the most used protein feature extractor. In this work we purpose a new approach to calculate amino acid composition values. The purpose of our method is to adjust the weights of the composition values during feature extraction process. It means that bigger composition values will contribute more to prediction function than smaller ones. Our experimental results showed that our method outperformed PseAAC.
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    Prediction of Protein-Protein Interactions Using An Effective Sequence Based Combined Method
    (ELSEVIER SCIENCE BV, 2018) Goktepe, Yunus Emre; Kodaz, Halife
    Proteins and their interactions play a key role in the realization of all cellular biological activities of organisms. Therefore, prediction of protein-protein interactions is crucial for elucidating biological processes. Experimental studies are inadequate for some reasons such as the time required to reveal interactions, the fact that it is an expensive way and the number of yet unknown interactions is too great. Thus, a number of computational methods have been developed to predict protein-protein interactions. Generally, many of these methods that produce good results cannot be used without additional biological information such as protein domains, protein structural information, gene neighborhoods, gene expressions, and phylogenetic profiles. Therefore, there is a need for computational methods that can successfully predict interactions using only protein sequences. In this study, we present a novel sequence-based computational model. We applied a new technique called weighted skip-sequential conjoint triads in the proposed method. The results of this research were evaluated on generally used databases and demonstrated its success in this field. (C) 2018 Elsevier B.V. All rights reserved.
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    TAG SNP SELECTION USING CLONAL SELECTION ALGORITHM BASED ON SUPPORT VECTOR MACHINE
    (AMER SOC MECHANICAL ENGINEERS, 2011) Ilhan, Ilhan; Goktepe, Yunus Emre; Ozcan, Cengiz; Kahramanli, Sirzat
    Investigations on genetic variants associated with complex diseases are important for enhancements in diagnosis and treatments. SNPs (Single Nucleotide Polymorphisms), which comprise most of the millions of changes in human genome, are promising tools for disease-gene association studies. On the other hand, these studies are limited by cost of genotyping tremendous number of SNPs. Therefore, it is essential to identify a subset of SNPs that represents rest of the SNPs. As subset of SNPs is identified, data set should be searched as well as possible. In this study, a new method called CLONTagger was introduced, where Support Vector Machine (SVM) was used as SNP prediction method, whereas Clonal Selection Algorithm (CLONALG) was used as tag SNP selection method. The suggested method was compared with current tag SNP selection algorithms in literature using different datasets. Experimental results demonstrated that the suggested method could identify tag SNPs with better prediction accuracy than other methods from literature.