MaSDA: a system for analyzing mass spectrometry data.

Mass spectrometry (MS) approaches have been recently coupled with advanced data analysis techniques in order to enable clinicians to discover useful knowledge from MS data. However, effectively and efficiently handling and analyzing MS data requires to take into account a number of issues. In particular, the huge dimensionality and the variety of noisy factors present in MS data require careful preprocessing and modeling phases in order to make them amenable to the further analysis. In this paper we present MaSDA, a system performing advanced analysis on MS data. MaSDA has the following main features: (i) it implements an approach of MS data representation that exploits a model based on low dimensional, dense time series; (ii) it provides a wide set of MS preprocessing operations which are accomplished by means of a user-friendly graphical tool; (iii) it embeds a number of tools implementing various tasks of data mining and knowledge discovery, in order to assist the user in taking critical clinical decisions. Our system has been experimentally tested on several publicly available datasets, showing effectiveness and efficiency in supporting advanced analysis of MS data.