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• W2901656981 startingPage "e0207334" @default.
• W2901656981 abstract "The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: Which experimental outputs should be measured to ensure that unique model parameters can be calculated?. Stated formally, we examine the topic of minimal output sets that guarantee a model's structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times." @default.
• W2901656981 created "2018-11-29" @default.
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• W2901656981 date "2018-11-12" @default.
• W2901656981 modified "2023-09-29" @default.
• W2901656981 title "Determining minimal output sets that ensure structural identifiability" @default.
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• W2901656981 doi "https://doi.org/10.1371/journal.pone.0207334" @default.
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