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Updated: Jul 11, 2026
07:35
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
An exploration into study design for biomarker identification: issues and recommendations
Jacqueline A Hall1, Robert Brown, Jim Paul
1Centre for Oncology and Applied Pharmacology, Cancer Research UK Beatson laboratories, University of Glasgow, Garscube estate, Glasgow, G61 1BD, UK. j.hall@beatson.gla.ac.uk
Cancer Genomics & Proteomics
|September 20, 2007
Summary
Identifying clinically relevant biomarkers from genomic data is challenging. This review highlights study design issues impacting biomarker discovery and offers recommendations for improving success rates in identifying impactful biomarkers.
Area of Science:
- Genomics
- Biostatistics
- Clinical Research
Background:
- Genomic profiling generates vast datasets, complicating the identification of biological processes linked to clinical outcomes.
- Numerous candidate biomarkers have been found, yet few achieve clinical validation or impact.
Purpose of the Study:
- To review study design challenges in data mining for biomarker identification.
- To illustrate how study design influences biomarker discovery results.
- To provide recommendations for enhancing the identification of clinically relevant biomarkers.
Main Methods:
- Focus on study design issues in biomarker identification, including clinical endpoint selection, statistical power, and significance.
- Examine study design for supervised clustering methods to identify gene networks associated with clinical outcomes.
Main Results:
- Study design significantly influences the outcomes of biomarker discovery efforts.
- Specific attention is given to the design of studies employing supervised clustering for gene network identification.
Conclusions:
- Addressing study design flaws is crucial for successful biomarker validation.
- Future research should prioritize robust study designs to increase the clinical utility of identified biomarkers.
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