Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/61208
Title: Knowledge discovery in spectral data by means of complex networks.
Author: Zanin M
Papo D
Solis JL
Espinosa JC
Frausto-Reyes C
Anda PP
Sevilla-Escoboza R
Jaimes-Reategui R
Boccaletti S
Menasalvas E
Sousa P
Issue Date: 2013
Abstract: 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.
URI: http://hdl.handle.net/20.500.12104/61208
ISSN: 2218-1989; 2218-1989
Appears in Collections:Producción científica UdeG (prueba)

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