Bag of Peaks: interpretation of NMR spectrometry (Articolo in rivista)

Type
Label
  • Bag of Peaks: interpretation of NMR spectrometry (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1093/bioinformatics/btn599 (literal)
Alternative label
  • Gavin Brelstaff; Manuele Bicego; Nicola Culeddu; Matilde Chessa (2009)
    Bag of Peaks: interpretation of NMR spectrometry
    in Bioinformatics (Oxf., Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gavin Brelstaff; Manuele Bicego; Nicola Culeddu; Matilde Chessa (literal)
Pagina inizio
  • 258 (literal)
Pagina fine
  • 264 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 25 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 2 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Biocomputing, CRS4, 09100 Pula (CA), Sardinia, DEIR, University of Sassari, via Torre Tonda 34, 07100 Sassari, ICB-CNR, 07040 Li Punti, Sassari and Porto Conte Ricerche, Loc. Tramariglio, Alghero, Italy (literal)
Titolo
  • Bag of Peaks: interpretation of NMR spectrometry (literal)
Abstract
  • Motivation: The analysis of high-resolution proton nuclear magnetic resonance ( NMR) spectrometry can assist human experts to implicate metabolites expressed by diseased biofluids. Here, we explore an intermediate representation, between spectral trace and classifier, able to furnish a communicative interface between expert and machine. This representation permits equivalent, or better, classification accuracies than either principal component analysis ( PCA) or multi-dimensional scaling ( MDS). In the training phase, the peaks in each trace are detected and clustered in order to compile a common dictionary, which could be visualized and adjusted by an expert. The dictionary is used to characterize each trace with a fixed-length feature vector, termed Bag of Peaks, ready to be classified with classical supervised methods. Results: Our small-scale study, concerning Type I diabetes in Sardinian children, provides a preliminary indication of the effectiveness of the Bag of Peaks approach over standard PCA and MDS. Consistently, higher classification accuracies are obtained once a sufficient number of peaks (> 10) are included in the dictionary. A large-scale simulation of noisy spectra further confirms this advantage. Finally, suggestions for metabolite-peak loci that may be implicated in the disease are obtained by applying standard feature selection techniques. (literal)
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