Commenced in January 2007
Paper Count: 31584
Unsupervised Classification of DNA Barcodes Species Using Multi-Library Wavelet Networks
Abstract:DNA Barcode provides good sources of needed information to classify living species. The classification problem has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use the similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. However, all the used methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. In fact, our method permits to avoid the complex problem of form and structure in different classes of organisms. The empirical data and their classification performances are compared with other methods. Evenly, in this study, we present our system which is consisted of three phases. The first one, is called transformation, is composed of three sub steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. Moreover, the second phase step is an approximation; it is empowered by the use of Multi Library Wavelet Neural Networks (MLWNN). Finally, the third one, is called the classification of DNA Barcodes, is realized by applying the algorithm of hierarchical classification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107898Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1768
 P. D. N. Hebert, A. Cywinska, S. L. Ball and JR. DeWaard, “Biological identifications through DNA barcodes”. Proc R Soc, 2003, pp. 313-321.
 E. Weitschek, G. Fiscon and G. Felici, “Supervised DNA Barcodes species classification: analysis, comparisons and results”, BioData Mining, vol. 7:4 2014, pp.2-18.
 M. Moftah, S. H. Abdel Aziz, S. Elramah and A. Favereaux, “Classification of Sharks in the Egyptian Mediterranean Waters Using Morphological and DNA Barcodes Approaches”, PLoS ONE, vol. 6, 2011, pp. 1-7.
 Ch. Lei, Ch. Yu-Mei, C. Ding-Cheng and S. Xiao-Wen, “DNA Barcodes and species and subspecies classification within genus Carassius”, Zoological Research,vol.33, 2012, pp. 463-472.
 R. Sandberg, G. Winberg, CI Bränden, A. Kaske, I ErnbergI and J. Cöster, “Capturing Whole - Genome characteristics in short sequences using a naive Bayesian classifer”, Genome Res, vol.11, 2001, pp. 1404- 1409.
 F. Zanoguera and M. Francesco, “Protein classification into domains of life using Markov chain models”, Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference, 2004, 0-7695-2194- 0/04
 C. Wu, M. Berry, Y.-S Fung and J. McLarty, “Neural Networks for Molecular Sequence Classification”, Proc Int Conf Intell Syst Mol Biol., 1993, pp. 429-437.
 S. Kumar Subramanian and D. N, “Artificial Neural Network Based Method for Classification of Gene Expression Data of Human Diseases along with Privacy Preserving”, International Journal of Computers & Technology, vol.4, 2013, pp. 722-730.
 S. bai Arniker and H. Keung Kwan, “Advanced Numerical Representation of DNA Sequences”, International Conference on Bioscience, Biochemistry and Bioinformatics IPCBEE, vol.31, 2012, pp.1-5.
 W. Bellil, C. Ben Amar and M. Adel Alimi, “Beta Wavelet Based Image Compression”, International Conference on Signal, System and Design, SSD03, Tunisia, vol.1, 2003, pp.77-82.
 W. Bellil, C. Ben Amar and Mohamed AA: Synthesis of wavelet filters using wavelet neural networks”, Transactions on Engineering, Computation and Technology 2006, vol.13, pp. 108-111.
 C. Ben Amar, M. Zied and M. Adel Alimi, “Beta wavelets. Synthesis and application to lossy image compression”, Journal of Advances in Engineering Software, Elsevier Edition, vol.36, 2005, pp. 459 – 474.
 W. Bellil, C. Ben Amar and M. Adel Alimi,”Synthesis of wavelet filters using wavelet neural networks”, Transactions on Engineering, Computation and Technology, vol.13, 2006, pp. 108-111.
 C. Ben Amar, W. Bellil and M. Adel Alimi, “Beta Function and its Derivatives: A New Wavelet Family”, Transactions on Systems, Signals and Devices, vol.1, 2006, pp. 275-293.
 W. Bellil, C. Ben Amar and M. Adel Alimi, “Beta wavelets networks for function approximation”, International Conference on Adaptative and Natural Computing Algorithms, ICANNGA05, Coimbra Portugal, Springer Wien NewYork, 2005, pp. 18-21.
 M. Zaied, O. Jemai and C. Ben Amar, “Training of the Beta wavelet networks by the frames theory: Application to face recognition”, ieeexplore.ieee.org, Image Processing Theory, Tools & Applications, 2008, 978-1-4244-3322-3/08/.
 M. Zaied, C. Ben Amar and M. Adel Alimi, “Beta wavelet Networks for Face recognition”, Journal of decision systems, vol.14, 2005, pp.109- 122.