Yazar "Delibas, Emre" seçeneğine göre listele

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    A new feature vector model for alignment-free DNA sequence similarity analysis
    (Yildiz Technical Univ, 2022) Delibas, Emre; Arslan, Ahmet
    Improvements in technology have triggered the production of big data. Within this scope, enormous amounts of biological data have been generated. A number of analysis methods have been developed to access the information contained in biological data. DNA sequence analysis has drawn particular attention in recent years. As an alternative to alignment-based sequence comparison methods that have high computational costs, alignment-free comparison methods have emerged. These methods can calculate sequence similarity by applying different dimensions of numerical characterizations. In this paper, we propose a novel alignment-free DNA sequence analysis method based on a feature extraction strategy. The method utilizes numerical characterization and is implemented by calculating mean distance of the transitions, mean distance of the nucleotide duplications, and the base frequencies. The method then measures the similarity between 7-dimensional vectors that are obtained through feature extraction. Using this approach, we conducted a sequence similarity analysis of two different DNA sequence datasets of different lengths to demonstrate the effectiveness of the method. The proposed method shows that a simple and successful feature vector can be obtained when DNA sequences having many properties are used in combination with appropriate and effective descriptors. With this strategy, reasonable results were obtained with a low computational cost.
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    A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up
    (Elsevier Science Inc, 2020) Delibas, Emre; Arslan, Ahmet; Seker, Abdulkadir; Diri, Banu
    DNA sequence similarity analysis is an essential task in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods, which result in high computational cost, alignment-free methods have emerged that calculate similarity by digitizing the sequence in a different space. In this paper, we proposed an alignment-free DNA sequence similarity analysis method based on top-k n-gram matches, with the prediction that common repeating DNA subsections indicate high similarity between DNA sequences. In our method, we determined DNA sequence similarities by measuring similarity among feature vectors created according to top-k n-gram match-up scores without the use of similarity functions. We applied the similarity calculation for three different DNA data sets of different lengths. The phylogenetic relationships revealed by our method show that our trees coincide almost completely with the results of the MEGA software, which is based on sequence alignment. Our findings show that a certain number of frequently recurring common sequence patterns have the power to characterize DNA sequences. (C) 2020 Elsevier Inc. All rights reserved.
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    DNA Sequence Compression within Traditional Text Compression Algorithms
    (IEEE, 2017) Seker, Abdulkadir; Delibas, Emre; Diri, Banu
    It aimed that traditional text compression methods are using on compression of DNA sequence in this study. It has seen that the random short repeats are vital and it has examined their posivite impact for compression. A pipelined system with multiple algorithms running sequentially for compression. How the contribution of the algorithm to the system was investigated and especially the effect of the BWT on compression was shown. Results show that the pipeline system was found unable to catch the compression success of the Huffman coding alone.
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    DNA sequence similarity analysis using image texture analysis based on first-order statistics
    (Elsevier Science Inc, 2020) Delibas, Emre; Arslan, Ahmet
    Similarity is one of the key processes of DNA sequence analysis in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. One major task in alignment-free DNA sequence similarity calculations is to develop novel mathematical descriptors for DNA sequences. In this paper, we present a novel approach to DNA sequence similarity analysis studies using similarity calculations of texture images. Texture analysis methods, which are a subset of digital image processing methods, are used here with the assumption that these calculations can be adapted to alignment-free DNA sequence similarity analysis methods. Gray-level textures were created by the values assigned to the nucleotides in the DNA sequences. Similarity calculations were made between these textures using histogram-based texture analyses based on first-order statistics. We obtained texture features for 3 different DNA data sets of different lengths, and calculated the similarity matrices. The phylogenetic relationships revealed by our method shows our trees to be similar to the results of the MEGA software, which is based on sequence alignment. Our findings show that texture analysis metrics can be used to characterize DNA sequences. (C) 2020 Elsevier Inc. All rights reserved.
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    Efficient TF-IDF method for alignment-free DNA sequence similarity analysis
    (Elsevier Science Inc, 2025) Delibas, Emre
    This study proposes a pioneering alignment-free approach for the analysis of DNA sequence similarity. The method employs the representation of DNA sequences as n-grams, a technique that involves the adaptation of the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to genomic data. The primary objective of this approach is to enhance the accuracy of the results while concomitantly reducing the computational costs of the process, by ascertaining the most informative n-grams. The approach adopted in this study successfully circumvents the limitations of both traditional alignment-based and alignment-free methods, thereby demonstrating a commendable level of performance. The proposed method was tested on three different datasets and achieved high agreement with reference phylogenetic trees in the AFProject benchmark system. The results demonstrate that TF-IDF-based similarity matrices effectively capture phylogenetic relationships and significantly reduce processing time. The high accuracy rates obtained prove that the method offers a scalable and robust alternative in large genomic datasets. The method demonstrates considerable potential in DNA sequence similarity analysis, exhibiting high accuracy and low computational cost.
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    Fabric Defect Detection using Deep Learning
    (IEEE, 2016) Seker, Abdulkadir; Peker, Kadir Askin; Yuksek, Ahmet Gurkan; Delibas, Emre
    Fabric defect detection have importance in terms of sectoral quality. Automatic systems are developed on the defect detection, in this regard many methods are tried to obtain high precision with image processing studies. In this study, deep learning which distinguishes with multi-layer architectures and reveals high achievement is applied to fabric defect detection. Autoencoder - a deep learning algorithm-that aimed to represent input data via compression or decompression is tried to detect defect of fabrics and it gains acceptable success. The vital goal of this study is to increase achievement of feature extraction by tuning up the autoencoder's input value and hyper parameters.