Yazar "Ilhan, Ilhan" seçeneğine göre listele

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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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    A genetic algorithm-support vector machine method with parameter optimization for selecting the tag SNPs
    (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2013) Ilhan, Ilhan; Tezel, Gulay
    SNPs (Single Nucleotide Polymorphisms) include millions of changes in human genome, and therefore, are promising tools for disease-gene association studies. However, this kind of studies is constrained by the high expense of genotyping millions of SNPs. For this reason, it is required to obtain a suitable subset of SNPs to accurately represent the rest of SNPs. For this purpose, many methods have been developed to select a convenient subset of tag SNPs, but all of them only provide low prediction accuracy. In the present study, a brand new method is developed and introduced as GA-SVM with parameter optimization. This method benefits from support vector machine (SVM) and genetic algorithm (GA) to predict SNPs and to select tag SNPs, respectively. Furthermore, it also uses particle swarm optimization (PSO) algorithm to optimize C and gamma parameters of support vector machine. It is experimentally tested on a wide range of datasets, and the obtained results demonstrate that this method can provide better prediction accuracy in identifying tag SNPs compared to other methods at present. (c) 2012 Elsevier Inc. All rights reserved.
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    How to Select Tag SNPs in Genetic Association Studies? The CLONTagger Method with Parameter Optimization
    (MARY ANN LIEBERT, INC, 2013) Ilhan, Ilhan; Tezel, Gulay
    Selection of genetic variants is a crucial first step in the rational design of studies aimed at explaining individual differences in susceptibility to complex human diseases or health intervention outcomes; for example, in the emerging fields of pharmacogenomics, nutrigenomics, and vaccinomics. While single nucleotide polymorphisms (SNPs) are frequently employed in these studies, the cost of genotyping a huge number of SNPs remains a limiting factor, particularly in low and middle income countries. Therefore, it is important to detect a subset of SNPs to represent the rest of SNPs with maximum possible accuracy. The present study introduces a new method, CLONTagger with parameter optimization, which uses Support Vector Machine (SVM) to predict the rest of SNPs and Clonal Selection Algorithm (CLONALG) to select tag SNPs. Furthermore, the Particle Swarm Optimization algorithm is preferred for the optimization of C and gamma parameters of the Support Vector Machine. Additionally, using many datasets, we compared the proposed new method with the tag SNP selection algorithms present in literature. Our results suggest that the CLONTagger with parameter optimization can identify tag SNPs with better prediction accuracy than other methods. Application-oriented studies are warranted to evaluate the utility of this method in future research in human genetics and study of the genetic components of variable responses to drugs, nutrition, and vaccines.
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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.
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    Tag SNP selection using clonal selection and majority voting algorithms
    (INDERSCIENCE ENTERPRISES LTD, 2016) Ilhan, Ilhan; Tezel, Gulay
    Researchers should select a suitable subgroup that includes all SNPs and represents the rest of the SNPs with little error for very large-scale association studies. The SNPs included in the subgroup are tag SNPs or haplotype tag SNPs (htSNPs). When selecting the tag SNPs, it is critical to accurately predict and identify the smallest number of tag SNPs with minimum error. This study used the Clonal Selection Algorithm (CLONALG) to decide on the tag SNPs to be included in the subgroup. In addition, the study proposed a new method called CSMV, which used the Majority Voting (MV) method to predict the rest of the SNPs. This method was compared with the BPSO method and the CLONTagger with parameter optimisation method using datasets of different sizes. According to the experimental results of the study, the CSMV method could determine the tag SNPs with significantly higher accuracy than the other two methods.
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    Tag SNP Selection Using Similarity Associations Between SNPs
    (IEEE, 2015) Ilhan, Ilhan; Tezel, Gulay; Ozcan, Cengiz
    Genetic changes that may be associated with complex diseases are tried to be determined by means of many genome-wide association studies. Single Nucleotide Polymorphisms ( SNPs) are used primarily in these studies since they comprise a large part of these genetic changes. Statistical importance of the genome-wide association study is directly related to the number of individuals and SNPs. However, it is still very costly and time-consuming to genotype all SNPs inside the candidate area for many individuals in very large-scale association studies. For this reason, with a small error, it is necessary to select an appropriate subset of all SNPs that will represent the rest of SNPs. These selected SNPs are called tag SNPs or haplotype tag SNPs ( tag SNPs or htSNPs). It is essential in tag SNP selection to determine minimum tag SNP set with very good prediction accuracy. In this study, while Clonal Selection Algorithm ( CLONALG) was used as tag SNP selection method, a new method named CLONSim, in which similarity association between SNPs was used as the prediction method for the rest of SNPs was proposed. The proposed method was compared with BPSO ( Binary Particle Swarm Optimization) and CLONTagger methods with parameter optimization using datasets of different sizes. Experiment results showed that the proposed method could identify tag SNPs significantly faster.