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Review
. 2015 Apr;9(3-4):437-43.
doi: 10.1002/prca.201400107. Epub 2015 Jan 12.

Identifying novel biomarkers through data mining-a realistic scenario?

Johannes Griss  1 , Yasset Perez-Riverol, Henning Hermjakob, Juan Antonio Vizcaíno
Affiliations
Review

Identifying novel biomarkers through data mining-a realistic scenario?

Johannes Griss et al. Proteomics Clin Appl. 2015 Apr.

Abstract

In this article we discuss the requirements to use data mining of published proteomics datasets to assist proteomics-based biomarker discovery, the use of external data integration to solve the issue of inadequate small sample sizes and finally, we try to estimate the probability that new biomarkers will be identified through data mining alone.

Keywords: Bioinformatics; Biomarker; Data mining; Databases; Mass spectrometry.

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Figures

Figure 1
Figure 1
Current structure of the ProteomeXchange data workflow. Submissions have to contain the experiment's raw data, metadata, and the researcher's processed results (e.g. the identification data). ProteomeCentral is the portal for all ProteomeXchange datasets, independently from the receiving repository.
Figure 2
Figure 2
Frequency of clinical dataset types found in Proteome‐Xchange per receiving repository (by September 6, 2014).
See this image and copyright information in PMC

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