title: Feature selection in the reconstruction of complex network representations of spectral data.
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.
authors.affiliation: Faculdade de Ciências e Tecnologia, Departamento de Engenharia Electrotécnica, Universidade Nova de Lisboa, Lisboa, Portugal ; Innaxis Foundation and Research Institute, Madrid, Spain.
authors.forename: Massimiliano
authors.initials: M
authors.lastname: Zanin
authors.forename: Ernestina
authors.initials: E
authors.lastname: Menasalvas
authors.forename: Stefano
authors.initials: S
authors.lastname: Boccaletti
authors.forename: Pedro
authors.initials: P
authors.lastname: Sousa
mesh_terms: D000465:Algorithms; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000032:analysis* / Q000145:classification; D012372:ROC Curve; D015203:Reproducibility of Results
chemical_list: D020543:Proteome