Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra | Zendy

Dante Mantini | Zendy; Francesca Petrucci | Zendy; Piero Del Boccio | Zendy; Damiana Pieragostino | Zendy; Marta Di Nicola | Zendy; Alessandra Lugaresi | Zendy; Giorgio Federici | Zendy; Paolo Sacchetta | Zendy; Carmine Di Ilio | Zendy; Andrea Urbani | Zendy

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Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra

Author(s) -

Dante Mantini,

Francesca Petrucci,

Piero Del Boccio,

Damiana Pieragostino,

Marta Di Nicola,

Alessandra Lugaresi,

Giorgio Federici,

Paolo Sacchetta,

Carmine Di Ilio,

Andrea Urbani

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm533

Subject(s) - independent component analysis , matlab , computer science , pattern recognition (psychology) , blind signal separation , signal (programming language) , artificial intelligence , data mining , biological system , biology , programming language , operating system , computer network , channel (broadcasting)

Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra.

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