The Modified DNA Identification Classification on Fuzzy Relation

Abstract:

Article Preview

We proposed a categorized method of DNA sequences matrix by FCM (fuzzy cluster means). FCM avoided the errors caused by the reduction of dimensions. It further reached comprehensive machine learning. In our experiment, there are 40 training data which are artificial samples, and we verify the proposed method with 182 natural DNA sequences. The result showed the proposed method enhanced the accuracy of the classification of genes from 76% to 93%.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

1275-1281

Citation:

Y. J. Hu et al., "The Modified DNA Identification Classification on Fuzzy Relation", Applied Mechanics and Materials, Vols. 48-49, pp. 1275-1281, 2011

Online since:

February 2011

Keywords:

Export:

Price:

$41.00

[1] Encyclopedia of Forensic Science DNA Republic of China, http: /www. cib. gov. tw/science/Science0201. aspx? DOC_ID=00007 (in chinese).

[2] M. L. Phillips, Crime Scene Genetics: Transforming Forensic Science through Molecular Technologies. BioScience, vol. 58, 484-489, (2008).

DOI: https://doi.org/10.1641/b580604

[3] P. W. Lisette, P. David, Noninvasive Genetic Sampling Tools for Wildlife Biologists: a review of applications and recommendations for accurate data collection, Journal of Wildl. Manage. 1419-1433. vol 69, (2005).

DOI: https://doi.org/10.2193/0022-541x(2005)69[1419:ngstfw]2.0.co;2

[4] P. H. William, F. Christophe, G. S. Brian, Fuzzy Species Among Recombinogenic Bacteria, Journal of BMC Bioinformatics, 3: 6, (2005).

[5] B. Hayes, The Invention of the Genetic Code, Journal of American Scientist-Computing Science, Jan. -Feb., (1998).

[6] E. E. May, Bits and Bases: An Analysis of Genetic Information Paradigms. Signals, Systems and Computers, (2007).

[7] H. Z. Geng, S. M. Wu, S. G. Ji, The Mathematical Modeling Contest, Beijing Science Press, 2007. (in chinese).

[8] M. Zhang, M. X. Cheng, T. J. Tarn, A Mathematical Formulation of DNA Computation, Journal of IEEE Transaction on Nanobioscience , vol. 5, no. 1, (2006).

[9] L. M. Adleman, Molecular Compution of Solutions to Combinatorial Problems, Journal of Science 1021-1024, VOL. 266, (1994).

[10] M. S. Yang, Y. J Hu, C. R. Lin, C. L. Lin, Segmentation techniques for Tissue Differentiation in MRI of Ophthalmology Using Fuzzy Clustering Algorithms, Journal of Magnetic Resonance Imaging, 173–179, VOL. 20, (2002).

DOI: https://doi.org/10.1016/s0730-725x(02)00477-0

[11] W. Xuan, Y. J. Yi, Random Basis to Classification and Fuzzy-C-Means Clustering Analysis Debris Flow Interpretation Issues, Journal of Water Conservation Technologies 4(1): 37-46, 2009. (in chinese).

[12] M. S. Yang, Z. H. Yang, Fuzzy Clustering and Its Applications, Blue Ocean Culture, 2009. (in chinese).

[13] J. C. Dunn, A Fuzzy Relative of The Isodata Process and Its Use in Detecting Compact, Well-Separated Couster., Journal of Cybernet, 3, 32-57, (1974).

DOI: https://doi.org/10.1080/01969727308546046

[14] R. Lan, P. R. Reeves, When Does A Clone Deserve A Name? A Perspective on Bacterial Species Based on Population Genetics. Journal of Trends Microbiol, 9(9), 419-424, (2001).

DOI: https://doi.org/10.1016/s0966-842x(01)02133-3

[15] J. L. Francisco, B. Armando, G. Fernando, C. Carlos, M. Antonio, Fuzzy Association Rules for Biological Data Analysis: A Case Study on Yeast, Journal of BMC Bioinformatics, 9, 107, (2008).

DOI: https://doi.org/10.1186/1471-2105-9-107

[16] F. Limin, M. Enzo, FLAME, A Novel Fuzzy Clustering Method For The Analysis of DNA Microarray Data, Journal of BMC Bioinformatics, 8, 3, (2007).

[17] L. C. Cheng, H. Kenneth, M. C. Chung, S. S. Grace, Uncovering Transcriptional Interactions Via an Adaptive Fuzzy Logic Approach, Journal of BMC Bioinformatics, 10: 400., (2009).

DOI: https://doi.org/10.1186/1471-2105-10-400

[18] K. C. Hsiao, C. H. Huang, K. L. Ng, Protein Structural Classes Prediction via Residues Environment Profile, Asian Journal of Health and Informatiom Sciences, Vol. 1 No. 3, pp.332-342, (2006).

[19] S. Y. Shin, I. H. Lee, D. Kim, B. T. Zhang, Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing., Critical Rev. Biochem. Journal of Molecular Bio, vol. 9, pp.143-158, (2005).

DOI: https://doi.org/10.1109/tevc.2005.844166

[20] K. Karplus, C. Barrett, R. Hughey, Hidden Markov Models for Detecting Remote Protein Homologies., Journal of BMC Bioinformatics, 14(10): 846-856, (1998).

DOI: https://doi.org/10.1093/bioinformatics/14.10.846

[21] Z. C. Fen, P. H. Wen, Biological Science Information Easily, Hop Kee Book Press, 2005. (in chinese).

[22] L. Luo, Z. H. Shao, Protein Secondary Structural Predict based on conditional random fields, Application Research of Computers, Vol. 26 No. 3, 832-839, 2009.

[23] Gene Expression and the Genetic Code, National Yang Ming University, general biology textbook online. (in chinese).

[24] Z. Chi, H. Yan, T. Phan, Fuzzy Algoritms With Application to Image Processing and Pattern Recognition, World Scientific Publish Co. Pte. Ltd, (1996).

[25] S. C. Lin, P. Q. Feng, Oh! Fuzzy. Fuzzy Analysis, 3rd Wave Press, 1994. (in chinese).

[26] L. A. Zadeh, Fuzzy sets. Information and Control, 8(3): 338-353, (1965).

[27] H. Zimmerman, Fuzzy Sets Theory and Its Applications Kluwer Academic Publishers, (2001).

[28] F. M. Liang, W. H. Wong, Evolutionary Monte Carlo for Protein Folding Simulation, Journal of Chemical Physicals, 3374-3380, (2001).

[29] L. Christos, P. Costas, P. Themis, et al., Sequence–Based Protein Structure Prediction Using a Reduced State-space Hidden markov model, Journal of Computer in Biology and Medicines, 37(9): 1211-1224, (2007).

DOI: https://doi.org/10.1016/j.compbiomed.2006.10.014

[30] S. J. Hua, Z. R. Sun, A Novel Method of Protein Secondary Secture Prediction With High Segment Overlap Measure: Support Vector Machine Approach,Journal of Molecular Biology , 308(2), 397-407, (2001).

DOI: https://doi.org/10.1006/jmbi.2001.4580

[31] J. J. Ward, L. J. McGUFFIN, B. F. Buxton, et al., Secondary Secture Prediction With Support Vector Machines Approach, Journal of BMC Bioinformatics, 19(3), 1650-1655, (2003).

DOI: https://doi.org/10.1093/bioinformatics/btg223

[32] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1980.

[33] H. Kim, H. Park, Protein Secondary Secture Prediction based on an Improved Support Vector Machines, Journal of Protein Engineering, 16(8), 553-560, (2003).

DOI: https://doi.org/10.1093/protein/gzg072

[34] D. T. Jones, Protein Secondary Structural Predict Based on Position Spectific Scoring Matrices, Journal of Molecular Biology, 292(2), 195-202, (1999).

[35] A. G. Murzin, S. E. Brenner, T. Hubbard, C. Chothia, SCOP:A Structural Classification of Protein Database for The Investigation of Sequences And Structures. Journal of Molecular Biology , 536-540, (1995).

[36] J. G. M. Wetmur, DNA Probes: Applications of The Principles of Nucleic Acid Hybridization, Critical Rev. Biochem. Journal of Molecular Bio, vol. 26, pp.227-259, (1991).

DOI: https://doi.org/10.3109/10409239109114069

[37] T. Hastie, R. Tibshirani, M. Eisen, A. Alizadeh, R. Levy , L. Staudt, W. C. Chan, D. Botsteinm, P. Brown, Gene Shaving As A Method for Identifying Distinct Sets of Genes With Similar Expression. , Journal of Genom Bio, 1: 1-21, (2000).