title: Opportunities and Challenges of Multiplex Assays: A Machine Learning Perspective.
abstract: Multiplex assays that allow the simultaneous measurement of multiple analytes in small sample quantities have developed into a widely used technology. Their implementation spans across multiple assay systems and can provide readouts of similar quality as the respective single-plex measures, albeit at far higher throughput. Multiplex assay systems are therefore an important element for biomarker discovery and development strategies but analysis of the derived data can face substantial challenges that may limit the possibility of identifying meaningful biological markers. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, in particular from the perspective of machine learning aimed at identification of predictive biological signatures.
authors.affiliation: Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J 5, Mannheim, 68159, Germany.
authors.forename: Junfang
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J 5, Mannheim, 68159, Germany. emanuel.schwarz@zi-mannheim.de.
authors.forename: Emanuel
authors.initials: E
authors.lastname: Schwarz
mesh_terms: D015982:Bias; D015415:Biomarkers; D015986:Confounding Factors, Epidemiologic; D023281:Genomics / Q000379:methods / Q000592:standards; D057166:High-Throughput Screening Assays; D006801:Humans; D000069550:Machine Learning; D025202:Molecular Diagnostic Techniques / Q000379:methods* / Q000592:standards*; D040901:Proteomics / Q000379:methods / Q000592:standards; D015203:Reproducibility of Results
chemical_list: D015415:Biomarkers
keywords: Bias; Biomarker discovery; Confounding; Machine learning; Multiplex