Nov 30, 2016

Identification and Clinical Translation of Biomarker Signatures: Statistical Considerations

Methods in Molecular Biology
Emanuel Schwarz

Abstract

Powerful machine learning tools exist to extract biological patterns for diagnosis or prediction from high-dimensional datasets. Simultaneous advances in high-throughput profiling technologies have led to a rapid acceleration of biomarker discovery investigations across all areas of medicine. However, the translation of biomarker signatures into clinically useful tools has thus far been difficult. In this chapter, several important considerations are discussed that influence such translation in the context of classifier design. These include aspects of variable selection that go beyond classification accuracy, as well as effects of variability on assay stability and sample size. The consideration of such factors may lead to an adaptation of biomarker discovery approaches, aimed at an optimal balance of performance and clinical translatability.

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Mentioned in this Paper

Biological Markers
Classification
2-Dimensional
Adaptation
High Throughput Analysis
Sample Size
Clinical Aspects
Knowledge Translation
Pharmaceutical Preparations
High-Throughput Chemical Assays

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