title: Mutual information optimization for mass spectra data alignment.
abstract: "Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.
authors.affiliation: Università degli Studi di Milano-Bicocca, Milan.
authors.forename: Italo
authors.initials: I
authors.lastname: Zoppis
authors.forename: Erica
authors.initials: E
authors.lastname: Gianazza
authors.forename: Massimiliano
authors.initials: M
authors.lastname: Borsani
authors.forename: Clizia
authors.initials: C
authors.lastname: Chinello
authors.forename: Veronica
authors.initials: V
authors.lastname: Mainini
authors.forename: Carmen
authors.initials: C
authors.lastname: Galbusera
authors.forename: Carlo
authors.initials: C
authors.lastname: Ferrarese
authors.forename: Gloria
authors.initials: G
authors.lastname: Galimberti
authors.forename: Sandro
authors.initials: S
authors.lastname: Sorbi
authors.forename: Barbara
authors.initials: B
authors.lastname: Borroni
authors.forename: Fulvio
authors.initials: F
authors.lastname: Magni
authors.forename: Marco
authors.initials: M
authors.lastname: Antoniotti
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Mauri
mesh_terms: D000544:Alzheimer Disease; D015415:Biomarkers / Q000032:analysis / Q000737:chemistry; D001798:Blood Proteins / Q000032:analysis / Q000737:chemistry*; D016022:Case-Control Studies; D030562:Databases, Protein; D006801:Humans; D007257:Information Theory; D013058:Mass Spectrometry / Q000379:methods*; D020543:Proteome / Q000032:analysis / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D015398:Signal Transduction
chemical_list: D015415:Biomarkers; D001798:Blood Proteins; D020543:Proteome