The Effect of Numerical Mapping Techniques on Performance in Genomic Research

Seda Nur Gülocak; Bihter Daş

doi:10.35377/saucis...1191850

Araştırma Makalesi

Yıl 2022, Cilt: 5 Sayı: 3, 315 - 340, 31.12.2022

Seda Nur Gülocak , Bihter Daş

https://doi.org/10.35377/saucis...1191850

Öz

Kaynakça

P. of P. G. B. of the C. for D. and P. G. M. Nei, M. Nei, S. Kumar, ve E. P. P. of B. M. Nei, Molecular Evolution and Phylogenetics. Oxford University Press, 2000.
M. Akhtar, J. Epps, ve E. Ambikairajah, “Signal Processing in Sequence Analysis: Advances in Eukaryotic Gene Prediction”, IEEE J. Sel. Top. Signal Process., vol. 2, no 3, pp 310-321, Jun. 2008, doi: 10.1109/JSTSP.2008.923854.
L. Das, J. K. Das, S. Mohapatra, ve S. Nanda, “DNA numerical encoding schemes for exon prediction: a recent history”, Nucleosides, Nucleotides & Nucleic Acids, c. 40, no 10, pp. 985-1017, Oct. 2021, doi: 10.1080/15257770.2021.1966797.
U. N. Wisesty, T. R. Mengko, ve A. Purwarianti, “Gene mutation detection for breast cancer disease: A review”, IOP Conf. Ser.: Mater. Sci. Eng., vol. 830, no 3, pp. 032051, Apr. 2020, doi: 10.1088/1757-899X/830/3/032051.
M. Raman Kumar ve N. K. Vaegae, “A new numerical approach for DNA representation using modified Gabor wavelet transform for the identification of protein coding regions”, Biocybernetics and Biomedical Engineering, vol. 40, no 2, pp. 836-848, Apr. 2020, doi: 10.1016/j.bbe.2020.03.007.
N. Yu, Z. Li, ve Z. Yu, “Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning”, Big Data Mining and Analytics, vol. 1, no 3, pp. 191-210, Sep. 2018, doi: 10.26599/BDMA.2018.9020018.
P. K. Kumari, “A Survey on Numerical Representation Of DNA Sequences”, Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, Apr. 2018, Erişim: 05 November 2021. [Online]. Access Adress: https://asianssr.org/index.php/ajct/article/view/417
L. Das, J. K. Das, S. Nanda, ve S. Mohapatra, “DNA Coding Sequence Prediction: A Review”, içinde 2018 International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC), Oct. 2018, vol. 1, pp. 1-6. doi: 10.1109/AESPC44649.2018.9033278.
M. Ahmad, L. T. Jung, ve A.-A. Bhuiyan, “From DNA to protein: Why genetic code context of nucleotides for DNA signal processing? A review”, Biomedical Signal Processing and Control, vol. 34, pp. 44-63, Apr. 2017, doi: 10.1016/j.bspc.2017.01.004.
X. Jin vd., “Similarity/dissimilarity calculation methods of DNA sequences: A survey”, Journal of Molecular Graphics and Modelling, vol. 76, pp. 342-355, Sep. 2017, doi: 10.1016/j.jmgm.2017.07.019.
G. Mendizabal-Ruiz, I. Román-Godínez, S. Torres-Ramos, R. A. Salido-Ruiz, ve J. A. Morales, “On DNA numerical representations for genomic similarity computation”, PLOS ONE, vol. 12, no 3, p. e0173288, Mar. 2017, doi: 10.1371/journal.pone.0173288.
S. Saini ve L. Dewan, “Comparison of Numerical Representations of Genomic Sequences: Choosing the Best Mapping for Wavelet Analysis”, Int. J. Appl. Comput. Math, vol. 3, no 4, pp. 2943-2958, Dec. 2017, doi: 10.1007/s40819-016-0277-1.
Mabrouk, M.S. Advanced Genomic Signal Processing Methods in DNA Mapping Schemes for Gene Prediction Using Digital Filters --Gene prediction, Digital filters, 3- Base periodicity, Exon, Intron, Bioinformatics, Genomic signal processing”, American Journal of Signal Processing, p. 13, 2017.
L. Das, J. K. Das, ve S. Nanda, “Identification of exon location applying kaiser window and DFT techniques”, içinde 2017 2nd International Conference for Convergence in Technology (I2CT), Apr. 2017, pp. 211-216. doi: 10.1109/I2CT.2017.8226123.
B. Das ve I. Turkoglu, “Classification of DNA sequences using numerical mapping techniques and Fourier transformation, Journal of the Faculty of Engineering and Arcitecture of Gazi University, 2016, doi: 10.17341/gazimmfd.278447.
M. Abo-Zahhad, S. M. Ahmed, ve S. A. Abd-Elrahman, “Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction”, IJITCS, vol. 6, no 8, pp. 51-57, July. 2014, doi: 10.5815/ijitcs.2014.08.07.
M. Abo-Zahhad, S. M. Ahmed, ve S. A. Abd-Elrahman, “Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques”, IJITCS, vol. 4, no 8, pp. 22-36, July. 2012, doi: 10.5815/ijitcs.2012.08.03.
H. K. Kwan, B. Y. M. Kwan, ve J. Y. Y. Kwan, “Novel methodologies for spectral classification of exon and intron sequences”, EURASIP Journal on Advances in Signal Processing, vol. 2012, no 1, p. 50, Feb 2012, doi: 10.1186/1687-6180-2012-50.
S. D. Sharma, K. Shakya, ve S. N. Sharma, “Evaluation of DNA mapping schemes for exon detection”, içinde 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), Mar. 2011, pp. 71-74. doi: 10.1109/ICCCET.2011.5762441.
F. Akalin, N. Yumusak, Classification of exon and intron regions obtained using digital signal processing techniques on the DNA genome sequencing with EfficientNetB7 architecture, GUMMFD, 37:3 (2022) 1355-1371.
F. Akalin and N. Yumuşak, “Classification of ALL and CML malignancies being among the main types of leukaemia with graph neural networks and fuzzy logic algorithm,” GUMMFD, Mar. 2022, doi: 10.17341/gazimmfd.1022624.
L. Das, S. Nanda, ve J. K. Das, “An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window”, Genomics, vol. 111, no 3, pp. 284-296, May. 2019, doi: 10.1016/j.ygeno.2018.10.008.
A. C. H. Choong ve N. K. Lee, “Evaluation of convolutionary neural networks modeling of DNA sequences using ordinal versus one-hot encoding method”, içinde 2017 International Conference on Computer and Drone Applications (IConDA), Nov. 2017, pp. 60-65. doi: 10.1109/ICONDA.2017.8270400.
N. Chakravarthy, A. Spanias, L. D. Iasemidis, ve K. Tsakalis, “Autoregressive Modeling and Feature Analysis of DNA Sequences”, EURASIP J. Adv. Signal Process., vol. 2004, no 1, pp. 952689, Jan. 2004, doi: 10.1155/S111086570430925X.
R. K. M ve N. K. Vaegae, “Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes”, Biomedical Signal Processing and Control, vol. 58, no. 101859, Ap. 2020, doi: 10.1016/j.bspc.2020.101859.
B. Das, S. Toraman, and İ. Türkoğlu, “A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks,” Turk J Elec Eng & Comp Sci, vol. 28, no. 4, pp. 1932–1948, Jul. 2020, doi: 10.3906/elk-1909-119.
P. Bernaola-Galván, I. Grosse, P. Carpena, J. L. Oliver, R. Román-Roldán, ve H. E. Stanley, “Finding Borders between Coding and Noncoding DNA Regions by an Entropic Segmentation Method”, Phys. Rev. Lett., vol. 85, no 6, pp. 1342-1345, Aug. 2000, doi: 10.1103/PhysRevLett.85.1342.
D. Nicorici ve J. Astola, “Segmentation of DNA into Coding and Noncoding Regions Based on Recursive Entropic Segmentation and Stop-Codon Statistics”, EURASIP J. Adv. Signal Process., vol. 2004, no 1, pp. 832471, Dec. 2004, doi: 10.1155/S1110865704309212.
N. Y. Song ve H. Yan, “Autoregressive modeling of DNA features for short exon recognition”, içinde 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 2010, pp. 450-455. doi: 10.1109/BIBM.2010.5706608.
Q. Zheng, T. Chen, W. Zhou, L. Xie, ve H. Su, “Gene prediction by the noise-assisted MEMD and wavelet transform for identifying the protein coding regions”, Biocybernetics and Biomedical Engineering, Vol. 41, no 1, 2021, doi: 10.1016/j.bbe.2020.12.005.
R. Harrison, Y. Li, ve I. Măndoiu, Ed., Bioinformatics Research and Applications: 11th International Symposium, ISBRA 2015 Norfolk, USA, June 7-10, 2015 Proceedings, c. 9096. Cham: Springer International Publishing, 2015. doi: 10.1007/978-3-319-19048-8.
Z. Abbas, H. Tayara, ve K. T. Chong, “4mCPred-CNN—Prediction of DNA N4-Methylcytosine in the Mouse Genome Using a Convolutional Neural Network”, Genes, vol. 12, no 2, Feb. 2021, doi: 10.3390/genes12020296.
P. Liò ve M. Vannucci, “Finding pathogenicity islands and gene transfer events in genome data”, Bioinformatics, vol. 16, no 10, pp. 932-940, Oct. 2000, doi: 10.1093/bioinformatics/16.10.932.
L. Zhang, F. Tian, S. Wang, ve X. Liu, “A novel coding method for gene mutation correction during protein translation process”, Journal of Theoretical Biology, vol. 296, pp. 33-40, Mar. 2012, doi: 10.1016/j.jtbi.2011.11.031.
F. Castro-Chavez, “Defragged Binary I Ching Genetic Code Chromosomes Compared to Nirenberg’s and Transformed into Rotating 2D Circles and Squares and into a 3D 100% Symmetrical Tetrahedron Coupled to a Functional One to Discern Start From Non-Start Methionines through a Stella Octangula”, J Proteome Sci Comput Biol, vol. 2012, no 1, pp. 3, 2012, doi: 10.7243/2050-2273-1-3.
M. Raman Kumar ve V. Naveen Kumar, “A Numerical Representation Method for a DNA Sequence Using Gray Code Method”, içinde Soft Computing for Problem Solving, Singapore, 2020, pp. 645-654. doi: 10.1007/978-981-15-0184-5_55.
L. Deng, H. Wu, X. Liu, ve H. Liu, “DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence”, International Journal of Molecular Sciences, vol. 22, no 11, Jan. 2021, doi: 10.3390/ijms22115521.
Q. Zhang, Z. Shen, ve D.-S. Huang, “Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network”, Sci Rep, vol. 9, no 1, p. 8484, June. 2019, doi: 10.1038/s41598-019-44966-x.
M. Randić, D. Butina, and J. Zupan, “Novel 2-D graphical representation of proteins,” Chemical Physics Letters, vol. 419, no. 4, pp. 528–532, Feb. 2006, doi: 10.1016/j.cplett.2005.11.091.
Z. Liu, B. Liao, W. Zhu, ve G. Huang, “A 2D graphical representation of DNA sequence based on dual nucleotides and its application”, International Journal of Quantum Chemistry, vol. 109, no 5, pp. 948-958, 2009, doi: 10.1002/qua.21919.
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The Effect of Numerical Mapping Techniques on Performance in Genomic Research

Yıl 2022, Cilt: 5 Sayı: 3, 315 - 340, 31.12.2022

Seda Nur Gülocak , Bihter Daş

https://doi.org/10.35377/saucis...1191850

Öz

In genomic signal processing applications, digitization of these signals is needed to process and analyze DNA signals. In the digitization process, the mapping technique to be chosen greatly affects the performance of the system for the genomic domain to be studied. The purpose of this review is to analyze how numerical mapping techniques used in digitizing DNA sequences affect performance in genomic studies. For this purpose, all digital coding techniques presented in the literature in the studies conducted in the last 10 years have been examined, and the numerical representations of these techniques are given in a sample DNA sequence. In addition, the frequency of use of these coding techniques in four popular genomic areas such as exon region identification, exon-intron classification, phylogenetic analysis, gene detection, and the min-max range of the performances obtained by using these techniques in that area are also given. This study is thought to be a guide for researchers who want to work in the field of bioinformatics.

Anahtar Kelimeler

Numerical mapping techniques, Genomic analysis, DNA encoding schemes, Genomic signal processing, DNA sequence

Kaynakça

P. of P. G. B. of the C. for D. and P. G. M. Nei, M. Nei, S. Kumar, ve E. P. P. of B. M. Nei, Molecular Evolution and Phylogenetics. Oxford University Press, 2000.
M. Akhtar, J. Epps, ve E. Ambikairajah, “Signal Processing in Sequence Analysis: Advances in Eukaryotic Gene Prediction”, IEEE J. Sel. Top. Signal Process., vol. 2, no 3, pp 310-321, Jun. 2008, doi: 10.1109/JSTSP.2008.923854.
L. Das, J. K. Das, S. Mohapatra, ve S. Nanda, “DNA numerical encoding schemes for exon prediction: a recent history”, Nucleosides, Nucleotides & Nucleic Acids, c. 40, no 10, pp. 985-1017, Oct. 2021, doi: 10.1080/15257770.2021.1966797.
U. N. Wisesty, T. R. Mengko, ve A. Purwarianti, “Gene mutation detection for breast cancer disease: A review”, IOP Conf. Ser.: Mater. Sci. Eng., vol. 830, no 3, pp. 032051, Apr. 2020, doi: 10.1088/1757-899X/830/3/032051.
M. Raman Kumar ve N. K. Vaegae, “A new numerical approach for DNA representation using modified Gabor wavelet transform for the identification of protein coding regions”, Biocybernetics and Biomedical Engineering, vol. 40, no 2, pp. 836-848, Apr. 2020, doi: 10.1016/j.bbe.2020.03.007.
N. Yu, Z. Li, ve Z. Yu, “Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning”, Big Data Mining and Analytics, vol. 1, no 3, pp. 191-210, Sep. 2018, doi: 10.26599/BDMA.2018.9020018.
P. K. Kumari, “A Survey on Numerical Representation Of DNA Sequences”, Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, Apr. 2018, Erişim: 05 November 2021. [Online]. Access Adress: https://asianssr.org/index.php/ajct/article/view/417
L. Das, J. K. Das, S. Nanda, ve S. Mohapatra, “DNA Coding Sequence Prediction: A Review”, içinde 2018 International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC), Oct. 2018, vol. 1, pp. 1-6. doi: 10.1109/AESPC44649.2018.9033278.
M. Ahmad, L. T. Jung, ve A.-A. Bhuiyan, “From DNA to protein: Why genetic code context of nucleotides for DNA signal processing? A review”, Biomedical Signal Processing and Control, vol. 34, pp. 44-63, Apr. 2017, doi: 10.1016/j.bspc.2017.01.004.
X. Jin vd., “Similarity/dissimilarity calculation methods of DNA sequences: A survey”, Journal of Molecular Graphics and Modelling, vol. 76, pp. 342-355, Sep. 2017, doi: 10.1016/j.jmgm.2017.07.019.
G. Mendizabal-Ruiz, I. Román-Godínez, S. Torres-Ramos, R. A. Salido-Ruiz, ve J. A. Morales, “On DNA numerical representations for genomic similarity computation”, PLOS ONE, vol. 12, no 3, p. e0173288, Mar. 2017, doi: 10.1371/journal.pone.0173288.
S. Saini ve L. Dewan, “Comparison of Numerical Representations of Genomic Sequences: Choosing the Best Mapping for Wavelet Analysis”, Int. J. Appl. Comput. Math, vol. 3, no 4, pp. 2943-2958, Dec. 2017, doi: 10.1007/s40819-016-0277-1.
Mabrouk, M.S. Advanced Genomic Signal Processing Methods in DNA Mapping Schemes for Gene Prediction Using Digital Filters --Gene prediction, Digital filters, 3- Base periodicity, Exon, Intron, Bioinformatics, Genomic signal processing”, American Journal of Signal Processing, p. 13, 2017.
L. Das, J. K. Das, ve S. Nanda, “Identification of exon location applying kaiser window and DFT techniques”, içinde 2017 2nd International Conference for Convergence in Technology (I2CT), Apr. 2017, pp. 211-216. doi: 10.1109/I2CT.2017.8226123.
B. Das ve I. Turkoglu, “Classification of DNA sequences using numerical mapping techniques and Fourier transformation, Journal of the Faculty of Engineering and Arcitecture of Gazi University, 2016, doi: 10.17341/gazimmfd.278447.
M. Abo-Zahhad, S. M. Ahmed, ve S. A. Abd-Elrahman, “Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction”, IJITCS, vol. 6, no 8, pp. 51-57, July. 2014, doi: 10.5815/ijitcs.2014.08.07.
M. Abo-Zahhad, S. M. Ahmed, ve S. A. Abd-Elrahman, “Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques”, IJITCS, vol. 4, no 8, pp. 22-36, July. 2012, doi: 10.5815/ijitcs.2012.08.03.
H. K. Kwan, B. Y. M. Kwan, ve J. Y. Y. Kwan, “Novel methodologies for spectral classification of exon and intron sequences”, EURASIP Journal on Advances in Signal Processing, vol. 2012, no 1, p. 50, Feb 2012, doi: 10.1186/1687-6180-2012-50.
S. D. Sharma, K. Shakya, ve S. N. Sharma, “Evaluation of DNA mapping schemes for exon detection”, içinde 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), Mar. 2011, pp. 71-74. doi: 10.1109/ICCCET.2011.5762441.
F. Akalin, N. Yumusak, Classification of exon and intron regions obtained using digital signal processing techniques on the DNA genome sequencing with EfficientNetB7 architecture, GUMMFD, 37:3 (2022) 1355-1371.
F. Akalin and N. Yumuşak, “Classification of ALL and CML malignancies being among the main types of leukaemia with graph neural networks and fuzzy logic algorithm,” GUMMFD, Mar. 2022, doi: 10.17341/gazimmfd.1022624.
L. Das, S. Nanda, ve J. K. Das, “An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window”, Genomics, vol. 111, no 3, pp. 284-296, May. 2019, doi: 10.1016/j.ygeno.2018.10.008.
A. C. H. Choong ve N. K. Lee, “Evaluation of convolutionary neural networks modeling of DNA sequences using ordinal versus one-hot encoding method”, içinde 2017 International Conference on Computer and Drone Applications (IConDA), Nov. 2017, pp. 60-65. doi: 10.1109/ICONDA.2017.8270400.
N. Chakravarthy, A. Spanias, L. D. Iasemidis, ve K. Tsakalis, “Autoregressive Modeling and Feature Analysis of DNA Sequences”, EURASIP J. Adv. Signal Process., vol. 2004, no 1, pp. 952689, Jan. 2004, doi: 10.1155/S111086570430925X.
R. K. M ve N. K. Vaegae, “Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes”, Biomedical Signal Processing and Control, vol. 58, no. 101859, Ap. 2020, doi: 10.1016/j.bspc.2020.101859.
B. Das, S. Toraman, and İ. Türkoğlu, “A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks,” Turk J Elec Eng & Comp Sci, vol. 28, no. 4, pp. 1932–1948, Jul. 2020, doi: 10.3906/elk-1909-119.
P. Bernaola-Galván, I. Grosse, P. Carpena, J. L. Oliver, R. Román-Roldán, ve H. E. Stanley, “Finding Borders between Coding and Noncoding DNA Regions by an Entropic Segmentation Method”, Phys. Rev. Lett., vol. 85, no 6, pp. 1342-1345, Aug. 2000, doi: 10.1103/PhysRevLett.85.1342.
D. Nicorici ve J. Astola, “Segmentation of DNA into Coding and Noncoding Regions Based on Recursive Entropic Segmentation and Stop-Codon Statistics”, EURASIP J. Adv. Signal Process., vol. 2004, no 1, pp. 832471, Dec. 2004, doi: 10.1155/S1110865704309212.
N. Y. Song ve H. Yan, “Autoregressive modeling of DNA features for short exon recognition”, içinde 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 2010, pp. 450-455. doi: 10.1109/BIBM.2010.5706608.
Q. Zheng, T. Chen, W. Zhou, L. Xie, ve H. Su, “Gene prediction by the noise-assisted MEMD and wavelet transform for identifying the protein coding regions”, Biocybernetics and Biomedical Engineering, Vol. 41, no 1, 2021, doi: 10.1016/j.bbe.2020.12.005.
R. Harrison, Y. Li, ve I. Măndoiu, Ed., Bioinformatics Research and Applications: 11th International Symposium, ISBRA 2015 Norfolk, USA, June 7-10, 2015 Proceedings, c. 9096. Cham: Springer International Publishing, 2015. doi: 10.1007/978-3-319-19048-8.
Z. Abbas, H. Tayara, ve K. T. Chong, “4mCPred-CNN—Prediction of DNA N4-Methylcytosine in the Mouse Genome Using a Convolutional Neural Network”, Genes, vol. 12, no 2, Feb. 2021, doi: 10.3390/genes12020296.
P. Liò ve M. Vannucci, “Finding pathogenicity islands and gene transfer events in genome data”, Bioinformatics, vol. 16, no 10, pp. 932-940, Oct. 2000, doi: 10.1093/bioinformatics/16.10.932.
L. Zhang, F. Tian, S. Wang, ve X. Liu, “A novel coding method for gene mutation correction during protein translation process”, Journal of Theoretical Biology, vol. 296, pp. 33-40, Mar. 2012, doi: 10.1016/j.jtbi.2011.11.031.
F. Castro-Chavez, “Defragged Binary I Ching Genetic Code Chromosomes Compared to Nirenberg’s and Transformed into Rotating 2D Circles and Squares and into a 3D 100% Symmetrical Tetrahedron Coupled to a Functional One to Discern Start From Non-Start Methionines through a Stella Octangula”, J Proteome Sci Comput Biol, vol. 2012, no 1, pp. 3, 2012, doi: 10.7243/2050-2273-1-3.
M. Raman Kumar ve V. Naveen Kumar, “A Numerical Representation Method for a DNA Sequence Using Gray Code Method”, içinde Soft Computing for Problem Solving, Singapore, 2020, pp. 645-654. doi: 10.1007/978-981-15-0184-5_55.
L. Deng, H. Wu, X. Liu, ve H. Liu, “DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence”, International Journal of Molecular Sciences, vol. 22, no 11, Jan. 2021, doi: 10.3390/ijms22115521.
Q. Zhang, Z. Shen, ve D.-S. Huang, “Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network”, Sci Rep, vol. 9, no 1, p. 8484, June. 2019, doi: 10.1038/s41598-019-44966-x.
M. Randić, D. Butina, and J. Zupan, “Novel 2-D graphical representation of proteins,” Chemical Physics Letters, vol. 419, no. 4, pp. 528–532, Feb. 2006, doi: 10.1016/j.cplett.2005.11.091.
Z. Liu, B. Liao, W. Zhu, ve G. Huang, “A 2D graphical representation of DNA sequence based on dual nucleotides and its application”, International Journal of Quantum Chemistry, vol. 109, no 5, pp. 948-958, 2009, doi: 10.1002/qua.21919.
A. T. M. Bari, M. Reaz, A. T. Islam, H.-J. Choi, ve B.-S. Jeong, “Effective Encoding for DNA Sequence Visualization Based on Nucleotide’s Ring Structure”, Evolutionary bioinformatics online, vol. 9, pp. 251-61, July. 2013, doi: 10.4137/EBO.S12160.
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Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Yazılım Mühendisliği (Diğer)
Bölüm	Makaleler
Yazarlar	Seda Nur Gülocak 0000-0003-1849-3555 Bihter Daş 0000-0002-2498-3297
Yayımlanma Tarihi	31 Aralık 2022
Gönderilme Tarihi	19 Ekim 2022
Kabul Tarihi	1 Kasım 2022
Yayımlandığı Sayı	Yıl 2022Cilt: 5 Sayı: 3

Kaynak Göster

IEEE	S. N. Gülocak ve B. Daş, “The Effect of Numerical Mapping Techniques on Performance in Genomic Research”, SAUCIS, c. 5, sy. 3, ss. 315–340, 2022, doi: 10.35377/saucis...1191850.

Makale Dosyaları

Tam Metin

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