Filtering maxRatio results with machine learning models increases quantitative PCR accuracy over the fit point method.

Journal: Journal of microbiological methods

PMID: 31809831

Abstract

With qPCR reaching thousands of reactions per run, assay validation needs automation. We applied support vector machine to qPCR analysis and we could identify reactions with 100% accuracy, dispensing them from further validation. We achieved a greatly reduced workload that could improve high-throughput qPCR analysis.

Authors

Luigi Marongiu

Department of Experimental Surgery - Cancer Metastasis, Medical Faculty Mannheim, Centre for Biomedicine and Medical Technology Mannheim (CBTM), Ludolf-Krehl-Str. 6, 68135 Mannheim, Ruprecht-Karls University of Heidelberg, Germany. Electronic address: luigi.marongiu@medma.uni-heidelberg.de.
Eric Shain

Grove Street Technology LLC, 459 Grove Street, Glencoe, IL 60022, United States.
Kevin Shain

652 NW 76th Street, Seattle, WA 98117, United States.
Heike Allgayer

Department of Experimental Surgery - Cancer Metastasis, Medical Faculty Mannheim, Centre for Biomedicine and Medical Technology Mannheim (CBTM), Ludolf-Krehl-Str. 6, 68135 Mannheim, Ruprecht-Karls University of Heidelberg, Germany.

Keywords

Algorithms Data Analysis Humans Machine Learning Models, Theoretical Real-Time Polymerase Chain Reaction Support Vector Machine Validation Studies as Topic

External Resources

View on PubMed Access via DOI PubMed (31809831)

Filtering maxRatio results with machine learning models increases quantitative PCR accuracy over the fit point method.

Abstract

Authors

Keywords

External Resources

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