Feature selection in the reconstruction of complex network representations of spectral data

Massimiliano Zanin, Ernestina Menasalvas, Stefano Boccaletti, Pedro Sousa

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude.
Original languageEnglish
Article numbere72045
JournalPlosOne
Volume8
Issue number8
DOIs
Publication statusPublished - 2013

Keywords

  • MASS-SPECTROMETRY
  • PROTEOMIC PATTERNS
  • MUTUAL INFORMATION
  • PROSTATE-CANCER
  • SERUM
  • SCIENCE
  • SYSTEMS

Fingerprint

Dive into the research topics of 'Feature selection in the reconstruction of complex network representations of spectral data'. Together they form a unique fingerprint.

Cite this