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Bayesian methods for proteomic biomarker development
Date Issued
2015-12
Date Available
2016-09-19T16:12:56Z
Abstract
The advent of liquid chromatography mass spectrometry has seen a dramatic increase in the amount of data derived from proteomic biomarker discovery. These experiments have seemingly identified many potential candidate biomarkers. Frustratingly, very few of these candidates have been evaluated and validated sufficiently such that that they have progressed to the stage of routine clinical use. It is becoming apparent that the statistical methods used to evaluate the performance of new candidate biomarkers are a major limitation in their development. Bayesian methods offer some advantages over traditional statistical and machine learning methods. In particular they can incorporate external information into current experiments so as to guide biomarker selection. Further, they can be more robustto over-fitting than other approaches, especially when the number of samples used for discovery is relatively small. In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.
Sponsorship
European Commission - Seventh Framework Programme (FP7)
Health Research Board
Irish Research Council
Science Foundation Ireland
Other Sponsorship
Irish Cancer Society
St Luke’s Institute for Cancer Research
Movember GAP1
Program for Research in Third Level Institutions
Type of Material
Journal Article
Publisher
Elsevier
Journal
EuPA Open Proteomics
Volume
9
Start Page
54
End Page
64
Copyright (Published Version)
2015 Elsevier
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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Scopus© citations
15
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