TY - JOUR
T1 - Knowledge Discovery in Spectral Data by Means of Complex Networks
AU - Zanin, Massimiliano
AU - Papo, David
AU - González Solís, José Luis
AU - Martínez Espinosa, Juan Carlos
AU - Frausto-Reyes, Claudio
AU - Palomares Anda, Pascual
AU - Sevilla-Escoboza, Ricardo
AU - Jaimes-Reategui, Rider
AU - Boccaletti, Stefano
AU - Menasalvas, Ernestina
AU - Sousa, Pedro
N1 - Publisher Copyright:
© 2013 by the authors; licensee MDPI, Basel, Switzerland.
PY - 2013/3
Y1 - 2013/3
N2 - 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.
AB - 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.
KW - Classification
KW - Complex networks
KW - Data mining
KW - Spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=85126742592&partnerID=8YFLogxK
U2 - 10.3390/metabo3010155
DO - 10.3390/metabo3010155
M3 - Artículo
AN - SCOPUS:85126742592
SN - 2218-1989
VL - 3
SP - 155
EP - 167
JO - Metabolites
JF - Metabolites
IS - 1
ER -