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Advances in Mathematical Physics
Volume 2013 (2013), Article ID 917153, 10 pages
Can Power Laws Help Us Understand Gene and Proteome Information?
1Institute of Engineering, Polytechnic of Porto, Department of Electrical Engineering, Rua Dr. António Bernardino de Almeida 431, 4200-072 Porto, Portugal
2National Health Institute, Biochemical Genetics Unit, Medical Genetics Center “Jacinto de Magalhães”, Praça Pedro Nunes 88, 4099-028 Porto, Portugal
Received 11 February 2013; Accepted 27 February 2013
Academic Editor: Dumitru Baleanu
Copyright © 2013 J. A. Tenreiro Machado et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- M. Tyers and M. Mann, “From genomics to proteomics,” Nature, vol. 422, pp. 193–1197, 2003.
- P. Nicodeme, T. Doerks, and M. Vingron, “Proteome analysis based on motif statistics,” Bioinformatics, vol. 18, no. 2, pp. S161–S171, 2002.
- J. Bock and D. Gough, “Whole-proteome interaction mining,” Bioinformatics, vol. 19, no. 1, pp. 125–134, 2003.
- E. Nabieva, K. Jim, A. Agarwal, B. Chazelle, and M. Singh, “Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps,” Bioinformatics, vol. 21, no. supplement 1, pp. i302–i310, 2005.
- D. Nelson and M. Cox, Lehninger Principles of Biochemistry, Worth Publishers, 3rd edition, 2000.
- International Union of Pure and Applied Chemistry, http://www.iupac.org/.
- S. Vinga and J. Almeida, “Alignment-free sequence comparison—a review,” Bioinformatics, vol. 19, no. 4, pp. 513–523, 2003.
- A. Costa, J. Machado, and M. Quelhas, “Histogram-based DNA analysis for the visualization of chromosome, genome and species information,” Bioinformatics, vol. 27, no. 9, pp. 1207–1214, 2011.
- O. Weiss, M. Jimenez-Montano, and H. Herzel, “Information content of protein sequences,” Journal of Theoretical Biology, vol. 206, no. 3, pp. 379–386, 2000.
- Q. Dai and T. Wang, “Comparison study on k-word statistical measures for protein: from sequence to ‘sequence space’,” BMC Bioinformatics, vol. 9, no. 394, pp. 1471–2105, 2008.
- C. Hemmerich and S. Kim, “A study of residue correlation within protein sequences and Its application to sequence classification,” EURASIP Journal on Bioinformatics and Systems Biology, vol. 2007, Article ID 87356, 2007.
- NCBI Genome Download/FTP, ftp://ftp.ncbi.nlm.nih.gov/genomes/H_sapiens/CHR_01/.
- A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Review, vol. 51, no. 4, pp. 661–703, 2009.
- C. M. A. Pinto, A. Mendes Lopes, and J. A. T. Machado, “A review of power laws in real life phenomena,” Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 9, pp. 3558–3578, 2012.
- Universal Protein Resource, ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/.
- M. Kendall, “A new measure of rank correlation,” Biometrika, vol. 30, no. 1-2, pp. 81–89, 1938.
- D. Sculley, “Rank Aggregation for Similar Items,” in Proceedings of the 7th SIAM International, SIAM, Philadelphia, Pa, USA, 2007.
- G. Jurman, S. Riccadonna, R. Visintainer, and C. Furlanello, “Canberra distance on ranked Lists,” in Proceedings of Advances in Ranking and Neural Information Processing Systems Workshop (NIPS '09), S. Agrawal, C. Burges, and K. Crammer, Eds., pp. 22–27.
- J. Lin, “Divergence measures based on the Shannon entropy,” Transactions on Information Theory, vol. 37, no. 1, pp. 145–151, 1991.
- S. H. Cha, “Taxonomy of nominal type histogram distance measures,” in Proceedings of the American Conference on Applied Mathematics 2008, pp. 325–330, WSEAS.
- I. Borg and P. Groenen, Modern multidimensional scaling, Springer Series in Statistics, Springer, New York, NY, USA, 1997, Theory and applications.
- GGobi software package, http://www.ggobi.org/.
- M. Huynen and E. Nimwegen, “The frequency distribution of gene family sizes in complete genomes,” Molecular Biology and Evolution, vol. 15, no. 5, pp. 583–589, 1998.
- J. Qian, N. Luscombe, and M. Gerstein, “Protein family and fold occurrence in genomes: power-law behaviour and evolutionary model,” Journal of Molecular Biology, vol. 313, no. 4, pp. 673–681, 2001.
- G. Karev, Y. Wolf, A. Rzhetsky, F. Berezovskaya, and E. Koonin, “Birth and death of protein domains: a simple model of evolution explains power law behavior,” BMC Evolutionary Biology, vol. 2, no. 18, 2002.
- R. Murray, D. Bender, V. Rodwell, K. Botham, P. Kennelly, and P. A. Weil, Harper's Illustrated Biochemistry, McGraw-Hill, 28th edition, 2009.
- Q. Pan, O. Shai, L. J. Lee, B. J. Frey, and B. J. Blencowe, “Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing,” Nature Genetics, vol. 40, no. 12, pp. 1413–1415, 2008.
- A. Arneodo, C. Vaillant, B. Audit, F. Argoul, Y. Aubenton-Carafa, and C. Thermes, “Multi-scale coding of genomic information: from DNA sequence to genome structure and function,” Physics Reports, vol. 498, pp. 45–188, 2010.
- UniProtn, http://www.uniprot.org/.