title: Identification and Clinical Translation of Biomarker Signatures: Statistical Considerations.
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.
authors.affiliation: Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany. emanuel.schwarz@zi-mannheim.de.
authors.forename: Emanuel
authors.initials: E
authors.lastname: Schwarz
mesh_terms: D015415:Biomarkers; D057166:High-Throughput Screening Assays / Q000379:methods / Q000592:standards; D006801:Humans; D000069550:Machine Learning; D025202:Molecular Diagnostic Techniques / Q000379:methods / Q000592:standards; D015203:Reproducibility of Results; D018401:Sample Size; D000073886:Scientific Experimental Error; D057170:Translational Research, Biomedical / Q000379:methods / Q000592:standards
chemical_list: D015415:Biomarkers
keywords: Biomarker discovery; Classification; Machine learning; Molecular diagnostics; Variable selection