GuidelineGenome biology2019
Guidelines for benchmarking of optimization-based approaches for fitting mathematical models.
Guideline in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Biologically informed neural network models are robust to spurious interactions via self-pruning.Bioinformatics (Oxford, England) · 2026Article
- Biologically informed neural network models are robust to spurious interactions via self-pruning.bioRxiv : the preprint server for biology · 2025Article
- IonBench: A benchmark of optimisation strategies for mathematical models of ion channel currents.PLoS computational biology · 2025Article
- The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction.IET systems biology · 2023Article
- A Practical Guide for the Efficient Formulation and Calibration of Large, Energy- and Rule-Based Models of Cellular Signal Transduction.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models.PLoS computational biology · 2022Article
- GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems.ACS synthetic biology · 2022Article
- Mini-batch optimization enables training of ODE models on large-scale datasets.Nature communications · 2022Article
- Benchmarking of numerical integration methods for ODE models of biological systems.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insufficient performance of optimization-based approaches for the fitting of mathematical models is still a major bottleneck in systems biology. In this article, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. Important aspects for achieving an increased level of evidence for benchmark results are discussed. Based on general guidelines for benchmarking in computational biology, a collection of tailored guidelines is presented for performing informative and unbiased benchmarking of optimization-based fitting approaches. Comprehensive benchmark studies based on these recommendations are urgently required for the establishment of a robust and reliable methodology for the systems biology community.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.