Paper:
Importance of Computational Intelligent in Proteomics
Kabir Mamun and Alok Sharma
School of Engineering & Physics, The University of the South Pacific, Fiji, Laucala Campus, Suva, Fiji
- [1] J. R. Schnell and J. J. Chou, “Structure and mechanism of the M2 proton channel of influenza A virus,” Nature No.451, pp. 591-595, 2008.
- [2] M. J. Berardi, W. M. Shih, S. C. Harrison, and J. J. Chou, “Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching,” Nature No.476, pp. 109-113, 2011.
- [3] B. OuYang, S. Xie, M. J. Berardi, X. Zhao, J. Dev, et al., “Unusual architecture of the p7 channel from hepatitis C virus,” Nature Vol.498, pp. 521-525, 2013.
- [4] K. C. Chou, “Structural bioinformatics and its impact to biomedical science,” Curr Med Chem Vol.11, pp. 2105-2134, 2004.
- [5] K. C. Chou, “Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein,” J Proteome Res Vol.4, pp. 1681-1686, 2005.
- [6] J. Lundström, L. Rychlewski, J. Bujnicki, and A. Elofsson, “Pcons: A neuralnetwork-based consensus predictor that improves fold recognition,” Protein Sci Vol.10, pp. 2354-2362, 2001.
- [7] Z.Wang, A. N. Tegge, and J. Cheng, “Evaluating the absolute quality of a single protein model using structural features and support vector machines,” Proteins Vol.75, pp. 638-647, 2009.
- [8] Z. Wang, J. Eickholt, and J. Cheng, “APOLLO: A quality assessment service for single and multiple protein models,” Bioinformatics Vol.27, pp. 1715-1716, 2011.
- [9] P. Benkert, S. C. Tosatto, and D. Schomburg, “QMEAN: A comprehensive scoring function for model quality assessment,” Proteins Vol.71, pp. 261-277, 2008.
- [10] T. J. P. Hubbard, A. Bart, S. E. Brenner, A. G. Murzin, and C. Chothia, “SCOP: a structural classification of proteins database,” Nucleic Acids Research, Vol.27, pp. 254-256, 1999.
- [11] C. Chothia, “One thousand families for the molecular biologist,” Nature, Vol.357, pp. 543-544, 1992.
- [12] A. S. Eldin, T. H. A. Soliman, M. E.Marie, and M. M. M. Ghareeb, “A Deep Glimpse into Protein Fold Recognition,” Int. J. of Sciences, Research Article, ISSN:2305-3925, Vol.2, June 2013.
- [13] J. D. Thompson, D. G. Higgins, and T. J. Gibson, “CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice,” Nucleic Acids Research, Vol.22, pp. 4673-4680, 1994.
- [14] A. Marti-Renom, M. S. Madhusudhan, and S. Andrej, “Alignment of protein sequences by their profiles,” Protein Science, Vol.13, pp. 1071-1087, 2004.
- [15] M. W. Craven, R. J. Mural, L. J. Hauser, and E. C. Uberbacher, “Predicting protein folding classes without overly relying on homology,” Proc. of Intelligent Systems in Molecular Biology (ISMB), Vol.3, pp. 98-106, 1995.
- [16] I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk, and S. H. Kim, “Recognition of a protein fold in the context of the structural classification of proteins (SCOP) classification,” Proteins, Vol.35, pp. 401-407, 1999.
- [17] C. H. Ding and I. Dubchak, “Multi-class protein fold recognition using support vector machines and neural networks,” Bioinformatics, Vol.7, pp. 349-358, 2001.
- [18] K. Marsolo, S. Parthasarathy, and C. Ding, “A multi-level approach to SCOP fold recognition,” Proc. of the Fifth IEEE Symp. on Bioinformatics and Bioengineering, pp. 57-64, 2005.
- [19] S. Y. M. Suganthan and P. N. Kalyanmoy, “Multi-class protein fold recognition using multi-objective evolutionary algorithms,” Proc. of the 2004 IEEE Symp. on Computational Intelligence in Bioinformatics and Computational Biology, pp. 61-66, 2004.
- [20] R. Apweiler, A. Bairoch, C. H. Wu, W. C. Barker, B. Boeckmann, S. Ferro, E. Gasteiger, H. Huang, R. Lopez, M. Magrane, M. J. Martin, D. A. Natale, C. O’Donovan, N. Redaschi, and L. L. Yeh, “UniProt: the universal protein knowledge base,” Nucleic Acids Research, Vol.32, pp. D115-D119, 2004.
- [21] H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne, “The Protein Data Bank,” Nucleic Acids Research, Vol.28, pp. 235-242, 2000.
- [22] P. Klein and C. Delisi, “Prediction of protein structural class from the amino-acid sequence,” Biopolymers Vol.25, pp. 1659-1672, 1986.
- [23] H. Nakashima, K. Nishikawa, and T. Ooi, “The folding type of a protein is relevant to the amino acid composition,” J. of Biochemistry, Vol.99, pp. 153-162, 1986.
- [24] K. C. Chou and C. T. Zhang, “Predicting protein-folding types by distance functions that make allowances for amino-acid interactions,” J. of Biological Chemistry Vol.269, pp. 22014-22020, 1994.
- [25] Y. Cai, “Is it a paradox or misinterpretation?,” Proteins Vol.43, pp. 336-338, 2001.
- [26] K. D. Kedarisetti, L. A. Kurgan, and S. Dick, “Classifier ensembles for protein structural class prediction with varying homology,” Biochemical and Biophysical Research Communication, Vol.348, pp. 981-988, 2006.
- [27] Z.-X. Wang and Z. Yuan, “How good is the prediction of protein structural class by the component-coupled method?,” Proteins Vol.38, pp. 165-175, 2000.
- [28] A. Sharma, J. Lyons, A. Dehzangi, and K. K. Paliwal, “A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition,” J. of Theoretical Biology, Vol.320, No.7, pp. 41-46, 2013.
- [29] A. Sharma, K. K. Paliwal, A. Dehzangi, J. Lyons, S. Imoto, and S. Miyano, “A Strategy to Select Suitable Physicochemical Attributes of Amino Acids for Protein Fold Recognition,” BMC Bioinformatics, Vol.14, No.233, pp. 1-11, 2013.
- [30] Y. Saeys, I. Inza, and P. Larranaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, Vol.23, No.19, pp. 2507-2517, DOI: 10.1093/bioinformatics/btm344, 2007.
- [31] K. C. Chou and G. M. Maggiora, “Domain structural class prediction,” Protein Engineering Vol.11, pp. 523-538, 1998.
- [32] A. Murzin, S. Brenner, T. Hubbard, and C. Chothia, “SCOP: a structural classification of protein database for the investigation of sequence and structures,” J. of Molecular Biology Vol.247, pp. 536-540, 1995.
- [33] C. T. Zhang and K. C. Chou, “An optimization approach to predicting protein structural class from amino-acid composition,” Protein Science Vol.1, pp. 401-408, 1992.
- [34] H. B. Shen, J. Yang, X.-J. Liu, and K. C. Chou, “Using supervised fuzzy clustering to predict protein structural classes,” Biochemical and Biophysical Research Communications, Vol.334, pp. 577-581, 2005.
- [35] J. Cornette, K. B. Cease, H. Margalit, J. L. Spouge, J. A. Berzofsky, and C. DeLisi, “Hydrophobicity scales and computational techniques for detecting amphipathic structures in protein,” J. of Molecular Biology Vol.195, pp. 659-685, 1987.
- [36] F. Eisenhaber, C. Frömmel, and P. Argos, “Prediction of secondary tructural content of proteins from their amino acid composition alone, II. The paradox with secondary structural class,” Proteins Vol.25, pp. 169-179, 1996.
- [37] J. Yu and X. Chen, “Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data,” Bioinformatics, Vol.21, pp. i487-i494, 2005.
- [38] Y. Cai, X. J. Liu, X. B. Xu, and K. C. Chou, “Support vector machines for prediction of protein domain structural class,” J. of Theoretical Biology Vol.221, pp. 115-120, 2003.
- [39] W.-S. Bu, Z.-P. Feng, Z. Zhang, and C.-T. Zhang, “Prediction of protein (domain) structural classes based on amino-acid index,” European J. of Biochemistry Vol.266, pp. 1043-1049, 1999.
- [40] L. Jin, W. Fang, and H. Tang, “Prediction of protein structural classes by a new measure of information discrepancy,” Computational Biology and Chemistry Vol.27, pp. 373-380, 2003.
- [41] B. Rost and S. O’Donoghue, “Sisyphus and prediction of protein structure,” CABIOS, Vol.13, pp. 345-356, 1997.
- [42] D. T. Jones, “Protein secondary structure prediction based on position-specific scoring matrices,” J. Mol. Biol. Vol.292, pp. 195-202, 1999.
- [43] S. Hua and Z. Sun, “Support vector machine approach for protein subcellular localization prediction,” Bioinform., Vol17, pp. 721-728, 2001.
- [44] Y. Guermeur, “Combining discriminant models with new multiclass SVMs,” Pattern Anal. and Applications, Vol.5, No.2, pp. 168-179, 2002.
- [45] S. Altschul, T. Madden, A. Shaffer, J. Zhang, Z. Zhang, W. Miller, and D. Lipman, “Gapped Blast and PSIBlast: A new generation of protein database search programs,” Nucleic Acids Res., Vol.25, pp. 3389-3402, 1997.
- [46] T. Szecsi and K. A. Mamun, “Situation Recognition Module for Hospital Robots,” in R. Scheidl and B. Jakoby (eds.), Proc. of the 13th Mechatronics Forum Biennial Int. Conf. (MECHATRONICS 2012), pp. 47-54, 2012.
- [47] N. Guex and M. C. Peitsch, “SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling,” Electrophoresis, Vol.18, pp. 2714-2723, 1997.
- [48] R. Sharan, I. Ulitsky, and R. Shamir, “Network-based prediction of protein function,” Mol Syst Biol Vol.3, No.88, DOI: 10.1038/msb4100129, 2007.
- [49] J. Prilusky, “OCA, a browser-database for protein structure/function,” 1996.
- [50] A. Sharma, S. Imoto, S. Miyano, and V. Sharma, “Null space based feature selection method for gene expression data,” Int. J. of Mach. Learning and Cybernetics Vol.3, No.4, pp. 269-276, 2012.
- [51] A. Sharma, S. Imoto, and S. Miyano, “A top-r feature selection algorithm for microarray gene expression data,” IEEE/ACM Trans. on Comp. Biol. and Bioinform., Vol.9, No.3, pp. 754-764, 2012.
- [52] A. Sharma, C. H. Koh, S. Imoto, and S. Miyano, “Strategy of finding optimal number of features on gene expression data,” Electronics Letters, Vol.47, No.8, pp. 480-482, 2011.
- [53] A. Sharma, S. Imoto, and S. Miyano, “A between-class overlapping filter-based method for transcriptome data analysis,” J. of Bioinform. and Comp. Biol., Vol.10, No.05, 2012.
- [54] A. Sharma, K. K. Paliwal, S. Imoto, and S. Miyano, “Principal component analysis using QR decomposition,” Int. J. of Mach. Learn. and Cyber., Vol.4, No.6, pp. 679-683, 2013.
- [55] A. Sharma and K. K. Paliwal, “A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices,” Pattern Recognition, Vol.45, No.6, pp. 2205-2213, 2012.
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