A Machine Learning Approach to Mass Spectra Classification with Unsupervised Feature Selection
Computational Intelligence Methods for Bioinformatics and Biostatistics
A Partial Set Covering Model for Protein Mixture Identification Using Mass Spectrometry Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gaussian mixture decomposition in the analysis of MALDI-TOF spectra
Expert Systems: The Journal of Knowledge Engineering
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Motivation: 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. Results: The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology. Availability: The MATLAB implementation of the method described in the article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/. Contact: a.urbani@unich.it