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Open AccessArticle

Knowledge Discovery in Spectral Data by Means of Complex Networks

Faculdade de Ciências e Tecnologia, Departamento de Engenharia Electrotécnica, Universidade Novade Lisboa, Portugal
Centre for Biomedical Technology, Polytechnic University of Madrid Pozuelo de Alarcón, 28223 Madrid, Spain
Innaxis Foundation & Research Institute, Jos´e Ortega y Gasset 20, 28006, Madrid, Spain
Biophysics and Biological Science Laboratory, Centro Universitario de los Lagos, Universidad de Guadalajara, 47460, Lagos de Moreno, Jalisco, Mexico
Biotechnology and Mechatronic Academy Instituto Politécnico Nacional-UPIIG, 36275, Silao de la Victoria, Guanajuato, Mexico
Centro de Investigaciones en Óptica, A. C. 20200, Aguascalientes, Mexico
Hospital Regional de Alta Especialización del Bajío 37660, León, Gto., Mexico
Author to whom correspondence should be addressed.
Metabolites 2013, 3(1), 155-167;
Received: 6 December 2012 / Revised: 5 February 2013 / Accepted: 5 March 2013 / Published: 11 March 2013
(This article belongs to the Special Issue Data Processing in Metabolomics)
In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease. View Full-Text
Keywords: complex networks; data mining; spectroscopy; classification complex networks; data mining; spectroscopy; classification
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Zanin, M.; Papo, D.; Solís, J.L.G.; Espinosa, J.C.M.; Frausto-Reyes, C.; Anda, P.P.; Sevilla-Escoboza, R.; Jaimes-Reategui, R.; Boccaletti, S.; Menasalvas, E.; Sousa, P. Knowledge Discovery in Spectral Data by Means of Complex Networks. Metabolites 2013, 3, 155-167.

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