Knowledge Discovery in Spectral Data by Means of Complex Networks
| Autor: | Claudio Frausto-Reyes, Rider Jaimes-Reátegui, José Luis González Solís, Pedro Sousa, Ernestina Menasalvas, Juan Carlos Martínez Espinosa, Massimiliano Zanin, Ricardo Sevilla-Escoboza, Pascual Palomares Anda, David Papo, Stefano Boccaletti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
spectroscopy
Computer science Matemáticas Endocrinology Diabetes and Metabolism lcsh:QR1-502 computer.software_genre 01 natural sciences Biochemistry Article lcsh:Microbiology NO 03 medical and health sciences complex networks data mining classification Knowledge extraction 0103 physical sciences 010306 general physics Representation (mathematics) Spectral data Molecular Biology 030304 developmental biology Structure (mathematical logic) 0303 health sciences Física Complex network ComputingMethodologies_PATTERNRECOGNITION Data mining Noise (video) computer |
| Zdroj: | Metabolites, Vol 3, Iss 1, Pp 155-167 (2013) Metabolites, ISSN 2218-1989, 2013-03, Vol. 3, No. 1 Archivo Digital UPM instname Metabolites; Volume 3; Issue 1; Pages: 155-167 Metabolites |
| ISSN: | 2218-1989 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|