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
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show all references

References:
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Nature, 405 (2000), 827-836. doi: 10.1038/35015701.  Google Scholar

[2]

Proc. Natl. Acad. Sci. USA, 104 (2007), 8685-8690. doi: 10.1073/pnas.0701361104.  Google Scholar

[3]

Science, 286 (1999), 531-537. doi: 10.1126/science.286.5439.531.  Google Scholar

[4]

Nature, 415 (2002), 530-536. doi: 10.1038/415530a.  Google Scholar

[5]

Nature Genetics, 33 (2003), 245-254. doi: 10.1038/ng1089.  Google Scholar

[6]

Nature Reviews Genetics, 5 (2004), 101-113. Google Scholar

[7]

Physics Reports, 424 (2006), 175-308. doi: 10.1016/j.physrep.2005.10.009.  Google Scholar

[8]

SIAM Review, 45 (2003), 167-256. doi: 10.1137/S003614450342480.  Google Scholar

[9]

Science, 177 (1972), 393-397. 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]

Statistical Applications in Genetics and Molecular Biology, 4 (2005) 45 pp..  Google Scholar

[12]

Chaos, 21 (2011), 033103. doi: 10.1063/1.3608126.  Google Scholar

[13]

The Journal of Machine Learning Research, 3 (2003), 1-48. Google Scholar

[14]

1st edition, Springer-Verlag, Berlin, 2006. Google Scholar

[15]

Pediatric Nephrology, 13 (1999), 612-619. doi: 10.1007/s004670050756.  Google Scholar

[16]

Urology Research, 27 (1999), 29-39. doi: 10.1007/s002400050086.  Google Scholar

[17]

Cell, 116 (2009), 281-297. doi: 10.1016/S0092-8674(04)00045-5.  Google Scholar

[18]

Cell, 136 (2009), 215-233. doi: 10.1016/j.cell.2009.01.002.  Google Scholar

[19]

Physica A, 314 (2002), 109-113. doi: 10.1016/S0378-4371(02)01089-0.  Google Scholar

[20]

3rd edition, MIT Press, New York, 2009.  Google Scholar

[21]

1st edition, McGraw-Hill, New York, 1960. Google Scholar

[22]

1st edition, Springer, Berlin, 2003. Google Scholar

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