Bayesian Methods for Proteomic Biomarker Development

Publication date: Available online 10 August 2015 Source:EuPA Open Proteomics Author(s): Belinda Hernández, Stephen R Pennington, Andrew C Parnell 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 robust to over-fitting than other approaches, especially when the number of samples used for discovery is relatively small. Graphical abstract
Source: EuPA Open Proteomics - Category: Bioinformatics Source Type: research