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SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI

Year 2016, Volume: 31 Issue: 4, 0 - 0, 14.12.2016
https://doi.org/10.17341/gazimmfd.278447

Abstract

Bir DNA diziliminde bulunan bazların bir araya gelerek oluşturdukları kombinasyonlar, o DNA dizilimindeki bir gene karşılık gelir ve bu genlerden de RNA kopya dizilimleri çıkarılır. Kopyalanan bu RNA’lar oluşurken genin baz dizilimi baştan sona tümüyle okunmaz, bir kısım dizilimin okunmasından ve RNA kopyasının çıkartılmasından sonra uzun bir bölüm okunmadan atlanır ve başka bir bölüme geçilir sonra kalınan yerden devam edilir. Genlerin okunmayan ve kodlanmayan bu bölümüne intron, kodlanan kısımlarına ise ekson denir. Bir DNA dizilimindeki protein nerede, ne kadar kodlanır? Büyüme ve gelişme nerede düzenlenir? Kök hücreler nerede başka hücreye dönüştürülür? Hücreler hangi durumlarda çoğaltılır? Tüm bu soruların cevabı ve kanser gibi genetik hastalıkların araştırılması DNA dizilimlerinin ekson ve intron olarak sınıflandırmasıyla mümkündür.

Bu makale çalışmasının amacı, bir DNA diziliminin ekson ve intron olarak sınıflandırılmasında farklı sayısal haritalama tekniklerinin performanslarını karşılaştırmaktır. Bu amaç doğrultusunda insan türünün MEFV genine ait DNA dizilimleri, 9 farklı haritalama tekniği ile sayısal dizilere dönüştürülmüştür. Dönüştürülen bu sayısal dizileri ekson ve intron olarak sınıflandırmak için Ayrık Fourier Dönüşümü Yöntemi kullanılmıştır. Ayrık Fourier Dönüşümü yönteminde 4 farklı pencereleme fonksiyonu kullanılmış ve bu pencereleme fonksiyonlarının da sınıflandırma başarımı üzerinde etkileri karşılaştırılmıştır. Ayrıca Fourier tabanlı yöntemle elde edilen sonuçlar makine öğrenme tabanlı Destek Vektör Makineleri ve K-En yakın komşu algoritması yöntemleri ile karşılaştırılmıştır. Ayrık Fourier Dönüşümü yönteminin İnteger haritalama tekniği ile %96,2 sınıflandırma başarım oranıyla diğer makine öğrenme yöntemlerine göre yüksek olduğu açıkça görülmektedir. Uygulama sonucunda Tamsayı Tekniği ve Paired Numerik Tekniği 4 pencereleme fonksiyonunda diğer sayısal tekniklere göre daha yüksek sınıflandırma performansı göstermiştir. Ayrıca Hamming pencereleme fonksiyonunda sınıflandırma başarısı diğer pencereleme fonksiyonlarından daha yüksek çıkmıştır.

References

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  • Kwan, J. Y. Y., Kwan, B. Y. M., Kwan, H. K., Spectral Analysis of Numerical Exon and Intron Sequences, Proceedings of IEEE International Conference on Bioinformatics and Biomedicine Workshops, Hong Kong, 876-877, 2010.
  • Marhon, S. A., Kremer, S. J., A dynamic representation-based, de novomethod for protein-coding region prediction and biological information detection, Elsevier, Digital Signal Processing 46, 10–18, 2015.
  • Zhang, J., Yang, C., DNA Sequence Recognition Based on the Markov Model, 6th International Conference on Biomedical Engineering and Informatics (BMEI 2013), 2013.
  • Mandal, S. B., Saha, S., Mandal, A., Roy, M., Prediction of Protein Coding Regions of a DNA Sequence through Spectral Analysis, IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision, 2012.
  • Xia, J., Caragea, D., Brown, S. J, Prediction of Alternatively Spliced Exons Using Support Vector Machines, Int. J. Data Mining and Bioinformatics, 4(4), 411-30, 2010.
  • Dror, G., Sorek, R., Shamir, R., Accurate Identification of Alternatively Spliced Exons Using Support Vector Machine, Bioinformatics, 21(7), 897-901, April 1, 2005.
  • Barman, S., Saha, S., Mandal, A., Roy, M., Prediction of protein coding regions of a DNA sequence through spectral analysis, Informatics, Electronics & Vision (ICIEV), 2012 International Conference, 18-19 May 2012.
  • Cristea, P. D., Genetic Signal Representation and Analysis, SPIE International Conference on Biomedical Optics Symposium, 4623, 77–84, 2002.
  • Chakravarthy, N., Spanias, A., Lasemidis, L. D., Tsakalis, K., Autoregressive Modeling and Feature Analysis of DNA Sequences, EURASIP Journal of Genomic Signal Processing, 1,13-28, January 2004.
  • Cristea, P. D., Genomic Signals of Reoriented ORFs, EURASIP J. Appl. Signal Process., 1, 132-137, 2004.
  • Berger, J. A., Mitra, S. K., Carli, M., Neri, A., New Approaches to Genome Sequence Analysis Based on Digital Signal Processing, IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS), 1-4, October 2002.
  • Cristea, P. D., Conversion of Nucleotides Sequences Into Genomic Signals, [J]. Cell. Mol. Med, 6, 279-303, April-June, 2002.
  • Dougherty, E. R., Hmulevich, I., Chen, J., Wang, Z. J., Genomic Signal Processing and Statistics, EURASIP Book Series in Signal Processing and Communications, Hindawi Pub. Corp, ISBN 977-5945-07-0, 2, 15-66, 2005.
  • Andersson, J. D., Doolittle, W. F., Nesbo, C. L., Are There Bugs in Our Genome?, Science, 292, 1848-1850, 2001.
  • Todd Holden, R., Subramaniam, R., Sullivan, E., Cheng, C., Sneider, G., Tremberger, J. A., Flamholz, D. H., Leiberman, and Cheung, T. D., ATCG Nucleotide Fluctuation of Deinococcus Radiodurans Radiation Genes, Proceedings of Society of Photo-Optical Instrumentation Engineers (SPIE), 669417, 1-10, August 2007.
  • Buldyrev, S. V., Dokholyan N. V., Goldberger, A. L., Havlin, S., Peng, C. K., Stanley, H. E., Viswanathan, G. M., Analysis of DNA Sequences Using Methods of Statistical Physics, Physica A, Elsevier, 249, 430-438, 1998.
  • Berger, J. A., Mitra, S. K., Carli, M., Neri, A., Visualization and Analysis of DNA Sequences Using DNA Walks, Journal of the Franklin Institute, 341, 37-53, January-March 2004.
  • Buldyrev, S.V., Goldberger, A. L., Havlin, S., Stanley, H. E., Long-Range Correlation Properties of Coding and Noncoding DNA Sequences: GenBank Analysis, Phy. Rev. E, 51(5), 5084-5091, May 1995.
  • Akhtar, M., Epps, J., Ambikairajah, E., Paired Spectral Content Measure for Gene and Exon Prediction in Eukaryotes, International Conference on Information and Emerging Technologies, ICIET‟07, 1- 4, July 2007.
  • Nair, A. S., Pillai, S. S., A Coding Measure Scheme Employing Electron-Ion Interaction Pseudo Potential (EIIP), Journal of Bio-information, 1, 197 – 202, October, 2006.
  • Chakraborty, S., Gupta, V., DWT Based Cancer Identification Using EIIP, 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), 12-13 February 2016.
  • Yee Kwan, J. Y., Ming Kwan B. Y., Keung Kwan H., Spectral Analysis of Numerical Exon ve Intron Sequences, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2010.
  • Shakya, D., K., Saxena, R., Sharma, S., N., An Adaptive Window Length Strategy for Eukaryotic CDS Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10, 1241 – 1252, 2 July 2013.
  • Datta, S., Asif, A., A Fast DFT Based Gene Prediction Algorithm For Identification of Protein Coding Regions, ICASSP, 5, 653–656, 2005.
  • Internet: Başkent Üniversitesi, http://www.baskent.edu.tr/~aerdamar/LAB1.pdf Erişim Tarihi: 01.01.2015.
  • Internet: İstanbul Teknik Üniversitesi, http://web.itu.edu.tr/~baykut/lab/pdf/Deney_3.pdf, Erişim Tarihi: 01.01.2015.
  • Saberkari, H., Shamsi, M., Sedaaghi, M., Golabi, F., Prediction of protein coding regions in DNA sequences using signal processing methods, Industrial Electronics and Applications (ISIEA), 2012 IEEE Symposium on, 23-26 September 2012.
  • Ramachandran, P., Lu, W. S., Antoniou, A., Filter-Based Methodology for the Location of Hot Spots in Proteins and Exons in DNA, IEEE Transactions on Biomedical Engineering, 59, 1598-1609, June 2012.
  • Oppenheim, A.V. and Schafer, R.W., Discrete Time Signal Processing, Prentice Hall, New Jersey, 1989.
  • Söderström, T. and Stoica, P., System Identification, Prentice Hall, Cambridge, 1989.
  • Kayran, A. H., Sayısal İşaret İşleme, İstanbul Teknik Üniversitesi, 1990.
  • Proakis, J. G. and Manolakis, D. G., Digital Signal Processing, Prentice Hall, New Jersey, 1996.
  • Avcı, K., Kaıser-Hamming Wındow And Its Performance Analysis For Nonrecursive Digital Filter Design, Journal of the Faculty of Engineering and Architecture of Gazi University, 29(4), 823-833, 2014.
  • Kaya, T., İnce, M. C., Design Of FIR Filter Using Modeled Window Function With Helping Of Artifıcial Neural Networks, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(3), 599-606, 2012.
  • Karaarslan, A., İskender, İ., A Novel Method In Power Factor Correction Circuits Using Average Current Control Technique And Digital Signal Processor, Journal of the Faculty of Engineering and Architecture of Gazi University, 26(1), 193-203, 2011.
  • Abo-Zahhad, M., Ahmed, S. M., Abd-Elrahman, A.S., Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques, International Journal Information Technology and Computer Science, 8, 22-36, 2012.
  • Hota, M. K., Srivastava, V. K., DSP Technique for Gene and Exon Prediction Taking Complex Indicator Sequence, Proc. IEEE TENCON, 1-6, 2008.
  • Sahu, S. ve Panda, G. Identification of Protein-Coding Regions in DNA Sequences Using A Time-Frequency Filtering Approach, Genomic Proteomics&Bioinformatics, October 2010.
  • Hota, M., Srivastava, V., Identification of Protein Coding Regions Using Antinotch Filter, Digital Signal Processing, 22, 869-877, June, 2012.
  • Vaidyanathan, P. P., Yoon, B. J., The Role of Signal-Processing Concepts in Genomics and Proteomics, J. Franklin Inst. 341, 111–135, 2004.
  • Vaidyanathan, P. P., Yoon, B. J., Gene and Exon Prediction Using Allpass-Based Flters, Workshop on Genomic Signal Process. Stat., Raleigh, NC, 2002.
  • Mena-Chalco, J., Carrer, H., Zana, Y., Cesar, R. M., Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform, IEEE/ACM Trans. Comput.Biol. Bioinformatic., 5, 198–207, 2008.
  • Kotlar, D., Levner, Y., Gene Prediction by Spectral Rotation Measure: A New Method for Identifying Protein-Coding Regions, Genome Res., 13, 1930–1937, 2003.
  • Ramachandran, P., Lu, W. S., Antoniou, A., Location of Exons in DNA Sequences Using Digital Flters, Proceedings of IEEE, 2337–2340, 2009.
  • Akhtar, M., Epps, J., Ambikairajah, E., Time and Frequency Domain Methods for Gene and Exon Prediction in Eukaryotes, Proc. IEEE ICASSP, 573–576, 2007.
  • Kwan, H. K., Arniker, S. B., Numerical Representation of DNA Sequences IEEE Inter, Conf. on Electro/Information Technology, EIT '09, Windsor, 307-310, 2009.
  • Cristea, P. D., Representation and analysis of DNA sequences. in Genomic signal Processing and Statistics, EURASIP Book Series in Signal Processing and Communications, (Eds) Edward R. Dougherty et al Hindawi Pub., 2, 15-66, 2005.
  • Kwan, J. Y. Y., Kwan, B. Y. M., Kwan, H. K., Novel Methodologies for Spectral Classification of Exon and Intron Sequences, EURASIP Journal on Advances in Signal Processing, 2012.
  • Das, B., Türkoğlu, İ., DNA Dizilimlerindeki Nükleotid Çiftlerinin Frekans Değerlerine Göre Farklı Sınıflandırma Yöntemleri ile Karşılaştırılması, Tıp Teknolojileri Ulusal Kongresi, 2014.
  • Law, N. F., Cheng, K., Siu, W., On Relationship of Z-Curve and Fourier Approaches for DNA Coding Sequence Classification, Bioinformation, 242-246, 2006.
  • Akhtar, M., Epps J., Ambikairajah, E., On DNA Numerical Representations for Period-3 Based Exon Prediction, IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS),1-4, June 2007.
  • Saberkari, H., Shamsi, M., Sedaaghi, M. H., Golabi, H., Prediction of protein coding regions in DNA sequences using signal processing methods, IEEE Symposium on Industrial Electronics and Aplications (ISIEA), September 23-26, Bandung Indonesia, 2012.
  • Zhang, L., Tian, F., Wang, S., A Modified Statistically Optimal Null Filter Method for Recognizing Protein-coding Regions, SciVerse ScienceDirect, Genomics Proteomics Bioinformatics 10, 166–173, 2012.
  • Ensembl Genbankası veritabanı, online erişim: http://www.ensembl.org
  • Yücesoy, E., Nabiev, V., Determination of a speaker’s age and gender with an SVM classifier based on GMM supervectors, Journal of the Faculty of Engineering and Architecture of Gazi University, 31(3), 501-509, 2016.
  • Sengur, A., Multiclass Least-Squares Support Vector Machines for Analog Modulation Classification, Expert Systems with Applications, 36(3), 6681-6685, 2009.
  • Yıldız, O., Tez, M., Bilge, H. Ş., Akçayol M. H., Güler, İ., Gene Selection For Breast Cancer Classification Based On Data Fusion And Genetic Algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(3), 659-668, 2012.
  • Kumar, M., Gromiha, M. M., Raghava, G. PS., Identification of DNA-Binding Proteins Using Support Vector Machines and Evolutionary Profiles, BMC Bioinformatics, 8:463, 1471-2105, 2007.
  • Kwan, B., YM., Kwan, J., YY., Kwan, H. K., Spectral Classification of Short Numerical Exon and Intron Sequences, BMC Bioinformatics, DOI: 10.1186/1471-2105-12-S11-A13, 2011.
Year 2016, Volume: 31 Issue: 4, 0 - 0, 14.12.2016
https://doi.org/10.17341/gazimmfd.278447

Abstract

References

  • Internet: http://schoolworkhelper.net/dna-mrna-introns-and-exons, Erişim Tarihi: 01.01.2015.
  • Kwan, J. Y. Y., Kwan, B. Y. M., Kwan, H. K., Spectral Analysis of Numerical Exon and Intron Sequences, Proceedings of IEEE International Conference on Bioinformatics and Biomedicine Workshops, Hong Kong, 876-877, 2010.
  • Marhon, S. A., Kremer, S. J., A dynamic representation-based, de novomethod for protein-coding region prediction and biological information detection, Elsevier, Digital Signal Processing 46, 10–18, 2015.
  • Zhang, J., Yang, C., DNA Sequence Recognition Based on the Markov Model, 6th International Conference on Biomedical Engineering and Informatics (BMEI 2013), 2013.
  • Mandal, S. B., Saha, S., Mandal, A., Roy, M., Prediction of Protein Coding Regions of a DNA Sequence through Spectral Analysis, IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision, 2012.
  • Xia, J., Caragea, D., Brown, S. J, Prediction of Alternatively Spliced Exons Using Support Vector Machines, Int. J. Data Mining and Bioinformatics, 4(4), 411-30, 2010.
  • Dror, G., Sorek, R., Shamir, R., Accurate Identification of Alternatively Spliced Exons Using Support Vector Machine, Bioinformatics, 21(7), 897-901, April 1, 2005.
  • Barman, S., Saha, S., Mandal, A., Roy, M., Prediction of protein coding regions of a DNA sequence through spectral analysis, Informatics, Electronics & Vision (ICIEV), 2012 International Conference, 18-19 May 2012.
  • Cristea, P. D., Genetic Signal Representation and Analysis, SPIE International Conference on Biomedical Optics Symposium, 4623, 77–84, 2002.
  • Chakravarthy, N., Spanias, A., Lasemidis, L. D., Tsakalis, K., Autoregressive Modeling and Feature Analysis of DNA Sequences, EURASIP Journal of Genomic Signal Processing, 1,13-28, January 2004.
  • Cristea, P. D., Genomic Signals of Reoriented ORFs, EURASIP J. Appl. Signal Process., 1, 132-137, 2004.
  • Berger, J. A., Mitra, S. K., Carli, M., Neri, A., New Approaches to Genome Sequence Analysis Based on Digital Signal Processing, IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS), 1-4, October 2002.
  • Cristea, P. D., Conversion of Nucleotides Sequences Into Genomic Signals, [J]. Cell. Mol. Med, 6, 279-303, April-June, 2002.
  • Dougherty, E. R., Hmulevich, I., Chen, J., Wang, Z. J., Genomic Signal Processing and Statistics, EURASIP Book Series in Signal Processing and Communications, Hindawi Pub. Corp, ISBN 977-5945-07-0, 2, 15-66, 2005.
  • Andersson, J. D., Doolittle, W. F., Nesbo, C. L., Are There Bugs in Our Genome?, Science, 292, 1848-1850, 2001.
  • Todd Holden, R., Subramaniam, R., Sullivan, E., Cheng, C., Sneider, G., Tremberger, J. A., Flamholz, D. H., Leiberman, and Cheung, T. D., ATCG Nucleotide Fluctuation of Deinococcus Radiodurans Radiation Genes, Proceedings of Society of Photo-Optical Instrumentation Engineers (SPIE), 669417, 1-10, August 2007.
  • Buldyrev, S. V., Dokholyan N. V., Goldberger, A. L., Havlin, S., Peng, C. K., Stanley, H. E., Viswanathan, G. M., Analysis of DNA Sequences Using Methods of Statistical Physics, Physica A, Elsevier, 249, 430-438, 1998.
  • Berger, J. A., Mitra, S. K., Carli, M., Neri, A., Visualization and Analysis of DNA Sequences Using DNA Walks, Journal of the Franklin Institute, 341, 37-53, January-March 2004.
  • Buldyrev, S.V., Goldberger, A. L., Havlin, S., Stanley, H. E., Long-Range Correlation Properties of Coding and Noncoding DNA Sequences: GenBank Analysis, Phy. Rev. E, 51(5), 5084-5091, May 1995.
  • Akhtar, M., Epps, J., Ambikairajah, E., Paired Spectral Content Measure for Gene and Exon Prediction in Eukaryotes, International Conference on Information and Emerging Technologies, ICIET‟07, 1- 4, July 2007.
  • Nair, A. S., Pillai, S. S., A Coding Measure Scheme Employing Electron-Ion Interaction Pseudo Potential (EIIP), Journal of Bio-information, 1, 197 – 202, October, 2006.
  • Chakraborty, S., Gupta, V., DWT Based Cancer Identification Using EIIP, 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), 12-13 February 2016.
  • Yee Kwan, J. Y., Ming Kwan B. Y., Keung Kwan H., Spectral Analysis of Numerical Exon ve Intron Sequences, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2010.
  • Shakya, D., K., Saxena, R., Sharma, S., N., An Adaptive Window Length Strategy for Eukaryotic CDS Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10, 1241 – 1252, 2 July 2013.
  • Datta, S., Asif, A., A Fast DFT Based Gene Prediction Algorithm For Identification of Protein Coding Regions, ICASSP, 5, 653–656, 2005.
  • Internet: Başkent Üniversitesi, http://www.baskent.edu.tr/~aerdamar/LAB1.pdf Erişim Tarihi: 01.01.2015.
  • Internet: İstanbul Teknik Üniversitesi, http://web.itu.edu.tr/~baykut/lab/pdf/Deney_3.pdf, Erişim Tarihi: 01.01.2015.
  • Saberkari, H., Shamsi, M., Sedaaghi, M., Golabi, F., Prediction of protein coding regions in DNA sequences using signal processing methods, Industrial Electronics and Applications (ISIEA), 2012 IEEE Symposium on, 23-26 September 2012.
  • Ramachandran, P., Lu, W. S., Antoniou, A., Filter-Based Methodology for the Location of Hot Spots in Proteins and Exons in DNA, IEEE Transactions on Biomedical Engineering, 59, 1598-1609, June 2012.
  • Oppenheim, A.V. and Schafer, R.W., Discrete Time Signal Processing, Prentice Hall, New Jersey, 1989.
  • Söderström, T. and Stoica, P., System Identification, Prentice Hall, Cambridge, 1989.
  • Kayran, A. H., Sayısal İşaret İşleme, İstanbul Teknik Üniversitesi, 1990.
  • Proakis, J. G. and Manolakis, D. G., Digital Signal Processing, Prentice Hall, New Jersey, 1996.
  • Avcı, K., Kaıser-Hamming Wındow And Its Performance Analysis For Nonrecursive Digital Filter Design, Journal of the Faculty of Engineering and Architecture of Gazi University, 29(4), 823-833, 2014.
  • Kaya, T., İnce, M. C., Design Of FIR Filter Using Modeled Window Function With Helping Of Artifıcial Neural Networks, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(3), 599-606, 2012.
  • Karaarslan, A., İskender, İ., A Novel Method In Power Factor Correction Circuits Using Average Current Control Technique And Digital Signal Processor, Journal of the Faculty of Engineering and Architecture of Gazi University, 26(1), 193-203, 2011.
  • Abo-Zahhad, M., Ahmed, S. M., Abd-Elrahman, A.S., Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques, International Journal Information Technology and Computer Science, 8, 22-36, 2012.
  • Hota, M. K., Srivastava, V. K., DSP Technique for Gene and Exon Prediction Taking Complex Indicator Sequence, Proc. IEEE TENCON, 1-6, 2008.
  • Sahu, S. ve Panda, G. Identification of Protein-Coding Regions in DNA Sequences Using A Time-Frequency Filtering Approach, Genomic Proteomics&Bioinformatics, October 2010.
  • Hota, M., Srivastava, V., Identification of Protein Coding Regions Using Antinotch Filter, Digital Signal Processing, 22, 869-877, June, 2012.
  • Vaidyanathan, P. P., Yoon, B. J., The Role of Signal-Processing Concepts in Genomics and Proteomics, J. Franklin Inst. 341, 111–135, 2004.
  • Vaidyanathan, P. P., Yoon, B. J., Gene and Exon Prediction Using Allpass-Based Flters, Workshop on Genomic Signal Process. Stat., Raleigh, NC, 2002.
  • Mena-Chalco, J., Carrer, H., Zana, Y., Cesar, R. M., Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform, IEEE/ACM Trans. Comput.Biol. Bioinformatic., 5, 198–207, 2008.
  • Kotlar, D., Levner, Y., Gene Prediction by Spectral Rotation Measure: A New Method for Identifying Protein-Coding Regions, Genome Res., 13, 1930–1937, 2003.
  • Ramachandran, P., Lu, W. S., Antoniou, A., Location of Exons in DNA Sequences Using Digital Flters, Proceedings of IEEE, 2337–2340, 2009.
  • Akhtar, M., Epps, J., Ambikairajah, E., Time and Frequency Domain Methods for Gene and Exon Prediction in Eukaryotes, Proc. IEEE ICASSP, 573–576, 2007.
  • Kwan, H. K., Arniker, S. B., Numerical Representation of DNA Sequences IEEE Inter, Conf. on Electro/Information Technology, EIT '09, Windsor, 307-310, 2009.
  • Cristea, P. D., Representation and analysis of DNA sequences. in Genomic signal Processing and Statistics, EURASIP Book Series in Signal Processing and Communications, (Eds) Edward R. Dougherty et al Hindawi Pub., 2, 15-66, 2005.
  • Kwan, J. Y. Y., Kwan, B. Y. M., Kwan, H. K., Novel Methodologies for Spectral Classification of Exon and Intron Sequences, EURASIP Journal on Advances in Signal Processing, 2012.
  • Das, B., Türkoğlu, İ., DNA Dizilimlerindeki Nükleotid Çiftlerinin Frekans Değerlerine Göre Farklı Sınıflandırma Yöntemleri ile Karşılaştırılması, Tıp Teknolojileri Ulusal Kongresi, 2014.
  • Law, N. F., Cheng, K., Siu, W., On Relationship of Z-Curve and Fourier Approaches for DNA Coding Sequence Classification, Bioinformation, 242-246, 2006.
  • Akhtar, M., Epps J., Ambikairajah, E., On DNA Numerical Representations for Period-3 Based Exon Prediction, IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS),1-4, June 2007.
  • Saberkari, H., Shamsi, M., Sedaaghi, M. H., Golabi, H., Prediction of protein coding regions in DNA sequences using signal processing methods, IEEE Symposium on Industrial Electronics and Aplications (ISIEA), September 23-26, Bandung Indonesia, 2012.
  • Zhang, L., Tian, F., Wang, S., A Modified Statistically Optimal Null Filter Method for Recognizing Protein-coding Regions, SciVerse ScienceDirect, Genomics Proteomics Bioinformatics 10, 166–173, 2012.
  • Ensembl Genbankası veritabanı, online erişim: http://www.ensembl.org
  • Yücesoy, E., Nabiev, V., Determination of a speaker’s age and gender with an SVM classifier based on GMM supervectors, Journal of the Faculty of Engineering and Architecture of Gazi University, 31(3), 501-509, 2016.
  • Sengur, A., Multiclass Least-Squares Support Vector Machines for Analog Modulation Classification, Expert Systems with Applications, 36(3), 6681-6685, 2009.
  • Yıldız, O., Tez, M., Bilge, H. Ş., Akçayol M. H., Güler, İ., Gene Selection For Breast Cancer Classification Based On Data Fusion And Genetic Algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(3), 659-668, 2012.
  • Kumar, M., Gromiha, M. M., Raghava, G. PS., Identification of DNA-Binding Proteins Using Support Vector Machines and Evolutionary Profiles, BMC Bioinformatics, 8:463, 1471-2105, 2007.
  • Kwan, B., YM., Kwan, J., YY., Kwan, H. K., Spectral Classification of Short Numerical Exon and Intron Sequences, BMC Bioinformatics, DOI: 10.1186/1471-2105-12-S11-A13, 2011.
There are 60 citations in total.

Details

Journal Section Makaleler
Authors

Bihter Daş

İbrahim Türkoğlu

Publication Date December 14, 2016
Submission Date June 12, 2015
Published in Issue Year 2016 Volume: 31 Issue: 4

Cite

APA Daş, B., & Türkoğlu, İ. (2016). SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 31(4). https://doi.org/10.17341/gazimmfd.278447
AMA Daş B, Türkoğlu İ. SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI. GUMMFD. December 2016;31(4). doi:10.17341/gazimmfd.278447
Chicago Daş, Bihter, and İbrahim Türkoğlu. “SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 31, no. 4 (December 2016). https://doi.org/10.17341/gazimmfd.278447.
EndNote Daş B, Türkoğlu İ (December 1, 2016) SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 31 4
IEEE B. Daş and İ. Türkoğlu, “SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI”, GUMMFD, vol. 31, no. 4, 2016, doi: 10.17341/gazimmfd.278447.
ISNAD Daş, Bihter - Türkoğlu, İbrahim. “SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 31/4 (December 2016). https://doi.org/10.17341/gazimmfd.278447.
JAMA Daş B, Türkoğlu İ. SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI. GUMMFD. 2016;31. doi:10.17341/gazimmfd.278447.
MLA Daş, Bihter and İbrahim Türkoğlu. “SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 31, no. 4, 2016, doi:10.17341/gazimmfd.278447.
Vancouver Daş B, Türkoğlu İ. SAYISAL HARİTALAMA TEKNİKLERİ VE FOURIER DÖNÜŞÜMÜ KULLANILARAK DNA DİZİLİMLERİNİN SINIFLANDIRILMASI. GUMMFD. 2016;31(4).