LIMPIC is a computational method intended for the detection of protein peaks from linear-mode MALDI-TOF data. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances.
Independent
component analysis (ICA) is used
for multi-subject studies of proteomic signals obtained by means of MALDI-TOF
mass spectrometry. ICA is a signal processing technique that can be utilized to
recover independent signals from a set of their linear mixtures. It showed to be
suitable the resolution of
overlapping protein signals in mass spectra, which are also contaminated by
baseline trend and background noise. The integration of ICA with
statistical tests for detecting the differences between experimental groups
allowed to identify protein peaks that could be indicators of a diseased state.
MASCAP is an integrated software suite intended for comparative biomarker detection from linear MALDI-TOF and SELDI-TOF MS data. It integrates algorithms for mass spectrometric data denoising, peak detection, statistical analysis and graphical plots for supporting the identification of significant protein peaks with high reliability and accuracy. The protein peaks that might be indicators of a specific diseased state are recovered by means of a differential comparison between the results obtained from the unhealthy and the control groups. This approach, mainly supported by statistical and visualization tools, simplifies biomarker detection, assisting the recognition of proteomic expression signatures of the disease.
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