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Robust conclusions in mass spectrometry analysis
- Source :
- ICCS
- Publication Year :
- 2015
- Publisher :
- Elsevier B.V., 2015.
-
Abstract
- 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.
- Subjects :
- Computer science
Data analysis
Inference
Machine learning
computer.software_genre
Mass spectrometry
Graph
Robustness (computer science)
Graph property
General Environmental Science
Random graph
Biological data
Settore INF/01 - Informatica
business.industry
Computer Science (all)
Robust decision
Data analysi
Robust decisions
General Earth and Planetary Sciences
Data mining
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICCS
- Accession number :
- edsair.doi.dedup.....ead8b535a7c4b1a0e592e183d80516fd