Robust conclusions in mass spectrometry analysis

Italo Zoppis, Riccardo Dondi, Massimiliano Borsani, Erica Gianazza, Clizia Chinello, Fulvio Magni, Giancarlo Mauri

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A central issue in biological data analysis is that uncertainty, resulting from different factors of variability, may change the effect of the events being investigated. Therefore, robustness is a fundamental step to be considered. Robustness refers to the ability of a process to cope well with uncertainties, but the different ways to model both the processes and the uncertainties lead to many alternative conclusions in the robustness analysis. In this paper we apply a framework allowing to deal with such questions for mass spectrometry data. Specifically, we provide robust decisions when testing hypothesis over a case/control population of subject measurements (i.e. proteomic profiles). To this concern, we formulate (i) a reference model for the observed data (i.e., graphs), (ii) a reference method to provide decisions (i.e., test of hypotheses over graph properties) and (iii) a reference model of variability to employ sources of uncertainties (i.e., random graphs). We apply these models to a realcase study, analyzing the mass spectrometry profiles of the most common type of Renal Cell Carcinoma; the Clear Cell variant.

Original languageEnglish
Title of host publicationProcedia Computer Science
Number of pages10
Publication statusPublished - 2015
EventInternational Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands
Duration: Apr 21 2002Apr 24 2002


OtherInternational Conference on Computational Science, ICCS 2002


  • Data analysis
  • Graph
  • Inference
  • Mass spectrometry
  • Robust decisions

ASJC Scopus subject areas

  • Computer Science(all)


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