Essential guidelines for computational method benchmarking

机译：计算方法基准测试的基本准则

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摘要

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.

机译：在计算生物学和其他科学领域，研究人员经常要在几种用于执行数据分析的计算方法之间进行选择。基准研究旨在使用特征明确的基准数据集严格比较不同方法的性能，确定每种方法的优势或就分析方法的适当选择提供建议。但是，必须仔细设计和实施基准研究，以提供准确，公正和有益的结果。在此，根据我们在计算生物学方面的经验，我们总结了进行高质量基准分析的关键实用指南和建议。

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期刊名称 Genome Biology
作者
Lukas M. Weber; Wouter Saelens; Robrecht Cannoodt; Charlotte Soneson; Alexander Hapfelmeier; Paul P. Gardner; Anne-Laure Boulesteix; Yvan Saeys; Mark D. Robinson;
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年(卷),期 2019(20),-1
年度 2019
页码 125
总页数 12
原文格式 PDF
正文语种
中图分类生物学;
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Essential guidelines for computational method benchmarking

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