September  2012, 7(3): 473-481. doi: 10.3934/nhm.2012.7.473

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, Spain

2. 

Faculty of Computer Science, Technical University of Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain

3. 

Departamento de Engenharia Electrotcnica, Faculdade de Ciencias e, Tecnologia Universidade Nova de Lisboa, Quinta da Torre, 2825 - 182 Caparica, Portugal

4. 

Centre for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain

Received  December 2011 Revised  July 2012 Published  October 2012

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.
Citation: Massimiliano Zanin, Ernestina Menasalvas, Pedro A. C. Sousa, Stefano Boccaletti. Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy. Networks & Heterogeneous Media, 2012, 7 (3) : 473-481. doi: 10.3934/nhm.2012.7.473
References:
[1]

D. J. Lockhart and E. A. Winzeler, Genomics, gene expression and DNA array,, Nature, 405 (2000), 827. doi: 10.1038/35015701. Google Scholar

[2]

K. I. Goh, et al., The human disease network,, Proc. Natl. Acad. Sci. USA, 104 (2007), 8685. doi: 10.1073/pnas.0701361104. Google Scholar

[3]

T. R. Golub, et al., Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring,, Science, 286 (1999), 531. doi: 10.1126/science.286.5439.531. Google Scholar

[4]

L. J. van 't Veer, et al., Gene expression profiling predicts clinical outcome of breast cancer,, Nature, 415 (2002), 530. doi: 10.1038/415530a. Google Scholar

[5]

R. Jaenisch and A. Bird, Epigenetic regulation of gene expression: How the genome integrates intrinsic and environmental signals,, Nature Genetics, 33 (2003), 245. doi: 10.1038/ng1089. Google Scholar

[6]

A. L. Barabási and Z. N. Oltvai, Network biology: Understanding the cell's functional organization,, Nature Reviews Genetics, 5 (2004), 101. Google Scholar

[7]

S. Boccaletti, V. Latora, Y. Moreno, M. Chavez and D. U. Hwang, Complex networks: Structure and dynamics,, Physics Reports, 424 (2006), 175. doi: 10.1016/j.physrep.2005.10.009. Google Scholar

[8]

M. E. J. Newman, The structure and function of complex networks,, SIAM Review, 45 (2003), 167. doi: 10.1137/S003614450342480. Google Scholar

[9]

P. W. Anderson, More is different,, Science, 177 (1972), 393. doi: 10.1126/science.177.4047.393. Google Scholar

[10]

L. da F. Costa, O. N. Oliveira Jr., G. Travieso, F. A. Rodrigues, P. R. Villas Boas, L. Antiqueira, M. P. Viana and L. E. C. da Rocha, Analyzing and modeling real-world phenomena with complex networks: A survey of applications,, preprint, (). Google Scholar

[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). Google Scholar

[12]

M. Zanin and S. Boccaletti, Complex networks analysis of Obstructive Nephropathy data,, Chaos, 21 (2011). doi: 10.1063/1.3608126. Google Scholar

[13]

I. Guyon and A. Elisseeff, An introduction to variable and feature selection,, The Journal of Machine Learning Research, 3 (2003), 1. Google Scholar

[14]

I. Guyon, S. Gunn, M. Nikravesh and L. A. Zadeh, "Feature Extraction-Foundations and Applications,", 1st edition, (2006). Google Scholar

[15]

R. L. Chevalier, Molecular and cellular pathophysiology of Obstructive Nephropathy,, Pediatric Nephrology, 13 (1999), 612. doi: 10.1007/s004670050756. Google Scholar

[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. doi: 10.1007/s002400050086. Google Scholar

[17]

D. P. Bartel, MicroRNAs: Genomics, biogenesis, mechanism, and function,, Cell, 116 (2009), 281. doi: 10.1016/S0092-8674(04)00045-5. Google Scholar

[18]

D. P. Bartel, MicroRNAs: Target recognition and regulatory functions,, Cell, 136 (2009), 215. doi: 10.1016/j.cell.2009.01.002. Google Scholar

[19]

V. Latora and M. Marchiori, Is the Boston subway a small-world network?,, Physica A, 314 (2002), 109. doi: 10.1016/S0378-4371(02)01089-0. Google Scholar

[20]

T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, "Introduction to Algorithms,", 3rd edition, (2009). Google Scholar

[21]

R. G. D. Steel and J. H. Torrie, "Principles and Procedures of Statistics,", 1st edition, (1960). Google Scholar

[22]

Karmeshu, "Entropy Measures, Maximum Entropy Principle and Emerging Applications,", 1st edition, (2003). Google Scholar

show all references

References:
[1]

D. J. Lockhart and E. A. Winzeler, Genomics, gene expression and DNA array,, Nature, 405 (2000), 827. doi: 10.1038/35015701. Google Scholar

[2]

K. I. Goh, et al., The human disease network,, Proc. Natl. Acad. Sci. USA, 104 (2007), 8685. doi: 10.1073/pnas.0701361104. Google Scholar

[3]

T. R. Golub, et al., Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring,, Science, 286 (1999), 531. doi: 10.1126/science.286.5439.531. Google Scholar

[4]

L. J. van 't Veer, et al., Gene expression profiling predicts clinical outcome of breast cancer,, Nature, 415 (2002), 530. doi: 10.1038/415530a. Google Scholar

[5]

R. Jaenisch and A. Bird, Epigenetic regulation of gene expression: How the genome integrates intrinsic and environmental signals,, Nature Genetics, 33 (2003), 245. doi: 10.1038/ng1089. Google Scholar

[6]

A. L. Barabási and Z. N. Oltvai, Network biology: Understanding the cell's functional organization,, Nature Reviews Genetics, 5 (2004), 101. Google Scholar

[7]

S. Boccaletti, V. Latora, Y. Moreno, M. Chavez and D. U. Hwang, Complex networks: Structure and dynamics,, Physics Reports, 424 (2006), 175. doi: 10.1016/j.physrep.2005.10.009. Google Scholar

[8]

M. E. J. Newman, The structure and function of complex networks,, SIAM Review, 45 (2003), 167. doi: 10.1137/S003614450342480. Google Scholar

[9]

P. W. Anderson, More is different,, Science, 177 (1972), 393. doi: 10.1126/science.177.4047.393. Google Scholar

[10]

L. da F. Costa, O. N. Oliveira Jr., G. Travieso, F. A. Rodrigues, P. R. Villas Boas, L. Antiqueira, M. P. Viana and L. E. C. da Rocha, Analyzing and modeling real-world phenomena with complex networks: A survey of applications,, preprint, (). Google Scholar

[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). Google Scholar

[12]

M. Zanin and S. Boccaletti, Complex networks analysis of Obstructive Nephropathy data,, Chaos, 21 (2011). doi: 10.1063/1.3608126. Google Scholar

[13]

I. Guyon and A. Elisseeff, An introduction to variable and feature selection,, The Journal of Machine Learning Research, 3 (2003), 1. Google Scholar

[14]

I. Guyon, S. Gunn, M. Nikravesh and L. A. Zadeh, "Feature Extraction-Foundations and Applications,", 1st edition, (2006). Google Scholar

[15]

R. L. Chevalier, Molecular and cellular pathophysiology of Obstructive Nephropathy,, Pediatric Nephrology, 13 (1999), 612. doi: 10.1007/s004670050756. Google Scholar

[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. doi: 10.1007/s002400050086. Google Scholar

[17]

D. P. Bartel, MicroRNAs: Genomics, biogenesis, mechanism, and function,, Cell, 116 (2009), 281. doi: 10.1016/S0092-8674(04)00045-5. Google Scholar

[18]

D. P. Bartel, MicroRNAs: Target recognition and regulatory functions,, Cell, 136 (2009), 215. doi: 10.1016/j.cell.2009.01.002. Google Scholar

[19]

V. Latora and M. Marchiori, Is the Boston subway a small-world network?,, Physica A, 314 (2002), 109. doi: 10.1016/S0378-4371(02)01089-0. Google Scholar

[20]

T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, "Introduction to Algorithms,", 3rd edition, (2009). Google Scholar

[21]

R. G. D. Steel and J. H. Torrie, "Principles and Procedures of Statistics,", 1st edition, (1960). Google Scholar

[22]

Karmeshu, "Entropy Measures, Maximum Entropy Principle and Emerging Applications,", 1st edition, (2003). Google Scholar

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