참고문헌
- Altschul, S. F., W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. 1990. Basic local alignment search tool. J. Mol. Biol. 215: 403-410.
- Caspi, R., T. Altman, K. Dreher, C. A. Fulcher, P. Subhraveti, I. M. Keseler, et al. 2012. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/ genome databases. Nucleic Acids Res. 40: D742-D753. https://doi.org/10.1093/nar/gkr1014
- Deb, K. and A. Raji Reddy. 2003. Reliable classification of two-class cancer data using evolutionary algorithms. Biosystems 72: 111-129. https://doi.org/10.1016/S0303-2647(03)00138-2
- Eddy, S. R. 1998. Profile hidden Markov models. Bioinformatics 14: 755-763. https://doi.org/10.1093/bioinformatics/14.9.755
- Finn, R. D., J. Mistry, J. Tate, P. Coggill, A. Heger, J. E. Pollington, et al. 2010. The Pfam protein families database. Nucleic Acids Res. 38: D211-D222. https://doi.org/10.1093/nar/gkp985
- Fischer, S., B. P. Brunk, F. Chen, X. Gao, O. S. Harb, J. B. Iodice, et al. 2011. Using OrthoMCL to assign proteins to OrthoMCL-DB groups or to cluster proteomes into new ortholog groups. Curr. Protoc. Bioinformatics 35: 6.12.1-6.12.19.
- Haft, D. H., B. J. Loftus, D. L. Richardson, F. Yang, J. A. Eisen, I. T. Paulsen, and O. White. 2001. TIGRFAMs: A protein family resource for the functional identification of proteins. Nucleic Acids Res. 29: 41-43. https://doi.org/10.1093/nar/29.1.41
- Karlik, B., M. O. Tokhi, and M. Alci. 2003. A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis. IEEE Trans. Biomed. Eng. 50: 1255-1261. https://doi.org/10.1109/TBME.2003.818469
- Keim, D. A., D. Oelke, R. Truman, and K. Neuhaus. 2006. Finding correlations in functionally equivalent proteins by integrating automated and visual data exploration, pp. 183-192. In: Proceedings of the Sixth IEEE Symposium on BioInformatics and BioEngineering, 16-18 October 2006. IEEE Computer Society Washington, DU, USA.
- Koski, L. B., M. W. Gray, B. F. Lang, and G. Burger. 2005. AutoFACT: An automatic functional annotation and classification tool. BMC Bioinformatics 6: 151. https://doi.org/10.1186/1471-2105-6-151
- Ludwig, W., O. Strunk, R. Westram, L. Richter, H. Meier, Yadhukumar, et al. 2004. ARB: A software environment for sequence data. Nucleic Acids Res. 32: 1363-1371. https://doi.org/10.1093/nar/gkh293
- Magrane, M. and U. Consortium. 2011. UniProt Knowledgebase: A hub of integrated protein data. Database (Oxford) 2011: bar009.
- Mardis, E. R. 2008. The impact of next-generation sequencing technology on genetics. Trends Genet. 24: 133-141. https://doi.org/10.1016/j.tig.2007.12.007
- Ma, Z., C. Zhou, L. Lu, Y. Ma, P. Sun, and Y. Cui. 2007. Predicting protein-protein interactions based on BP neural network, pp. 3-7. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2007. IEEE Computer Society Washington, DC, USA.
- McMillan, L. E. and A. C. Martin. 2008. Automatically extracting functionally equivalent proteins from SwissProt. BMC Bioinformatics 9: 418. https://doi.org/10.1186/1471-2105-9-418
- Michalopoulos, D. and C.-K. Hu. 2002. An error backpropagation artificial neural networks application in automatic car license plate recognition, pp. 1-8. In: Lecture Notes in Computer Science. Vol. 2358. Springer Berlin/Heidelberg.
- Moreno-Hagelsieb, G. and K. Latimer. 2008. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics 24: 319-324. https://doi.org/10.1093/bioinformatics/btm585
- Naik, A. D. and S. S. Bhagwat. 2005. Optimization of an artificial neural network for modeling protein solubility. J. Chem. Eng. Data 50: 460-467. https://doi.org/10.1021/je049713d
- Nair, T. M., S. S. Tambe, and B. D. Kulkarni. 1994. Application of artificial neural networks for prokaryotic transcription terminator prediction. FEBS Lett. 346: 273-277. https://doi.org/10.1016/0014-5793(94)00489-7
- Needleman, S. B. and C. D. Wunsch. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48: 443-453. https://doi.org/10.1016/0022-2836(70)90057-4
- Oh, S.-H. 2011. Error back-propagation algorithm for classification of imbalanced data. Neurocomputing 74: 1058-1061. https://doi.org/10.1016/j.neucom.2010.11.024
- Ponting, C. P. 2001. Issues in predicting protein function from sequence. Briefings Bioinformatics 2: 19-29. https://doi.org/10.1093/bib/2.1.19
- Smith, T. F. and M. S. Waterman. 1981. Identification of common molecular subsequences. J. Mol. Biol. 147: 195-197. https://doi.org/10.1016/0022-2836(81)90087-5
- Watson, J. D., R. A. Laskowski, and J. M. Thornton. 2005. Predicting protein function from sequence and structural data. Curr. Opin. Struct. Biol. 15: 275-284. https://doi.org/10.1016/j.sbi.2005.04.003
- Wilamowski, B. M. 2009. Neural network architectures and learning algorithms. Ind. Electron. Mag. IEEE 3: 56-63.
- Zhang, W., J. Chen, Y. Yang, Y. Tang, J. Shang, and B. Shen. 2011. A practical comparison of de novo genome assembly software tools for next-generation sequencing technologies. PLoS One 6: e17915. https://doi.org/10.1371/journal.pone.0017915
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