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Identifying novel biomarkers through data mining-a realistic scenario?

Johannes Griss1, Yasset Perez-Riverol, Henning Hermjakob

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Austria.

Proteomics. Clinical Applications
|October 29, 2014
PubMed
Summary

This study explores using data mining on published proteomics datasets for biomarker discovery. It addresses small sample sizes with external data integration and estimates the success rate of data mining alone for identifying new biomarkers.

Keywords:
BioinformaticsBiomarkerData miningDatabasesMass spectrometry

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Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Bioinformatics

Background:

  • Biomarker discovery is crucial for disease diagnosis and prognosis.
  • Small sample sizes in proteomics datasets limit the power of traditional analysis.
  • Integrating external data can augment limited sample sizes.

Purpose of the Study:

  • To outline requirements for data mining published proteomics datasets.
  • To investigate external data integration for overcoming small sample size limitations.
  • To estimate the probability of novel biomarker identification via data mining.

Main Methods:

  • Review of data mining methodologies applicable to proteomics.
  • Exploration of strategies for external data integration.
  • Statistical estimation of biomarker discovery success rates.

Main Results:

  • Defined requirements for effective data mining in proteomics.
  • Demonstrated the utility of external data integration for sample size augmentation.
  • Provided an estimation of the likelihood of biomarker discovery through data mining.

Conclusions:

  • Data mining of public proteomics data is a viable strategy for biomarker discovery.
  • External data integration is essential for robust analysis of limited datasets.
  • Further research can refine the predictive power of data mining for biomarker identification.