Year: 2019
Type: review
Abstract: In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.
Source: Genome biology
Institutions: SIB Swiss Institute of Bioinformatics, University of Zurich, Ghent University, VIB-UGent Center for Inflammation Research, Friedrich Miescher Institute, Technical University of Munich, University of Otago, Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,490
Funders: National Science Foundation, Silicon Valley Community Foundation, International Society for Advancement of Cytometry, Deutsche Forschungsgemeinschaft, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Bundesministerium für Bildung und Forschung, Fonds Wetenschappelijk Onderzoek, Universiteit Gent, Universität Zürich
Awards: Defining the identity and differentiation pathways of the immune-stimulating fibroblastic tumor stroma, G1386627085, The Structure and function of SLC26 transporters and volume regulated anion channels, G22550774, Beyond the average: computational tools for discovery in high-throughput single cell datasets, G3024871434, G3034827328, Verbundprojekt: MCML - Munich Center for Machine Learning, G5342848715, G6671980391, G8690818240, G8989249771
OpenAlex ID: W2902188679