2007/04/23 by Ryan N. Gutenkunst, Fergal Casey, Fergal P. Casey +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #Model Reduction and Neural Networks #q-bio.QM
paper · pdf · doi:10.1196/annals.1407.003
published as Annals of the New York Academy of Sciences 1115:203-211 (2007) · 4 pages, 2 figures. Submitted to the Annals of the New York Academy of Sciences for publication in "Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference"
arxiv created 2007/04/23 · openalex publication_date 2007/11/16 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.