Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy

Massimiliano Zanin; Ernestina Menasalvas; Pedro A. C. Sousa; Stefano Boccaletti; Massimiliano Zanin; Ernestina Menasalvas; Pedro A. C. Sousa; Stefano Boccaletti

doi:10.3934/nhm.2012.7.473

Networks and Heterogeneous Media

2012, Volume 7, Issue 3: 473-481. doi: 10.3934/nhm.2012.7.473

Previous Article Next Article

Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy

1.
Faculdade de Ciências e Tecnologia, Departamento de Engenharia Electrotécnica, Universidade Nova de Lisboa, Centre for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, 28223 Madrid
2.
Faculty of Computer Science, Technical University of Madrid, Pozuelo de Alarcón, 28223 Madrid
3.
Departamento de Engenharia Electrotcnica, Faculdade de Ciencias e, Tecnologia Universidade Nova de Lisboa, Quinta da Torre, 2825 - 182 Caparica
4.
Centre for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, 28223 Madrid

Received: 01 December 2011 Revised: 01 July 2012
Primary: 62-07, 92D10; Secondary: 92B05.

Many diseases have a genetic origin, and a great effort is being made to detect the genes that are responsible for their insurgence. One of the most promising techniques is the analysis of genetic information through the use of complex networks theory. Yet, a practical problem of this approach is its computational cost, which scales as the square of the number of features included in the initial dataset. In this paper, we propose the use of an iterative feature selection strategy to identify reduced subsets of relevant features, and show an application to the analysis of congenital Obstructive Nephropathy. Results demonstrate that, besides achieving a drastic reduction of the computational cost, the topologies of the obtained networks still hold all the relevant information, and are thus able to fully characterize the severity of the disease.
- Genetic data,
- complex networks,
- iterative feature selection,
- Obstructive Nephropathy
Citation: Massimiliano Zanin, Ernestina Menasalvas, Pedro A. C. Sousa, Stefano Boccaletti. Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy[J]. Networks and Heterogeneous Media, 2012, 7(3): 473-481. doi: 10.3934/nhm.2012.7.473

Related Papers:

Abstract

Many diseases have a genetic origin, and a great effort is being made to detect the genes that are responsible for their insurgence. One of the most promising techniques is the analysis of genetic information through the use of complex networks theory. Yet, a practical problem of this approach is its computational cost, which scales as the square of the number of features included in the initial dataset. In this paper, we propose the use of an iterative feature selection strategy to identify reduced subsets of relevant features, and show an application to the analysis of congenital Obstructive Nephropathy. Results demonstrate that, besides achieving a drastic reduction of the computational cost, the topologies of the obtained networks still hold all the relevant information, and are thus able to fully characterize the severity of the disease.

References

[1]	D. J. Lockhart and E. A. Winzeler, Genomics, gene expression and DNA array, Nature, 405 (2000), 827-836. doi: 10.1038/35015701
[2]	K. I. Goh, et al., The human disease network, Proc. Natl. Acad. Sci. USA, 104 (2007), 8685-8690. doi: 10.1073/pnas.0701361104
[3]	T. R. Golub, et al., Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science, 286 (1999), 531-537. doi: 10.1126/science.286.5439.531
[4]	L. J. van 't Veer, et al., Gene expression profiling predicts clinical outcome of breast cancer, Nature, 415 (2002), 530-536. doi: 10.1038/415530a
[5]	R. Jaenisch and A. Bird, Epigenetic regulation of gene expression: How the genome integrates intrinsic and environmental signals, Nature Genetics, 33 (2003), 245-254. doi: 10.1038/ng1089
[6]	A. L. Barabási and Z. N. Oltvai, Network biology: Understanding the cell's functional organization, Nature Reviews Genetics, 5 (2004), 101-113.
[7]	S. Boccaletti, V. Latora, Y. Moreno, M. Chavez and D. U. Hwang, Complex networks: Structure and dynamics, Physics Reports, 424 (2006), 175-308. doi: 10.1016/j.physrep.2005.10.009
[8]	M. E. J. Newman, The structure and function of complex networks, SIAM Review, 45 (2003), 167-256. doi: 10.1137/S003614450342480
[9]	P. W. Anderson, More is different, Science, 177 (1972), 393-397. doi: 10.1126/science.177.4047.393
[10]	preprint, arXiv:0711.3199.
[11]	B. Zhang and S. Horvath, A general framework for weighted gene co-expression network analysis, Statistical Applications in Genetics and Molecular Biology, 4 (2005) 45 pp..
[12]	M. Zanin and S. Boccaletti, Complex networks analysis of Obstructive Nephropathy data, Chaos, 21 (2011), 033103. doi: 10.1063/1.3608126
[13]	I. Guyon and A. Elisseeff, An introduction to variable and feature selection, The Journal of Machine Learning Research, 3 (2003), 1-48.
[14]	I. Guyon, S. Gunn, M. Nikravesh and L. A. Zadeh, "Feature Extraction-Foundations and Applications," 1^st edition, Springer-Verlag, Berlin, 2006.
[15]	R. L. Chevalier, Molecular and cellular pathophysiology of Obstructive Nephropathy, Pediatric Nephrology, 13 (1999), 612-619. doi: 10.1007/s004670050756
[16]	J. G. Wen, J. Frokiaer, T. M. Jorgensen and J. C. Djurhuus, Obstructive Nephropathy: An update of the experimental research, Urology Research, 27 (1999), 29-39. doi: 10.1007/s002400050086
[17]	D. P. Bartel, MicroRNAs: Genomics, biogenesis, mechanism, and function, Cell, 116 (2009), 281-297. doi: 10.1016/S0092-8674(04)00045-5
[18]	D. P. Bartel, MicroRNAs: Target recognition and regulatory functions, Cell, 136 (2009), 215-233. doi: 10.1016/j.cell.2009.01.002
[19]	V. Latora and M. Marchiori, Is the Boston subway a small-world network?, Physica A, 314 (2002), 109-113. doi: 10.1016/S0378-4371(02)01089-0
[20]	T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, "Introduction to Algorithms," 3^rd edition, MIT Press, New York, 2009.
[21]	R. G. D. Steel and J. H. Torrie, "Principles and Procedures of Statistics," 1^st edition, McGraw-Hill, New York, 1960.
[22]	Karmeshu, "Entropy Measures, Maximum Entropy Principle and Emerging Applications," 1^st edition, Springer, Berlin, 2003.

Reader Comments

Your name:*

Email:*
© 2012 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)