ArticleF1000Research2016
Unit testing, model validation, and biological simulation.
Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- The five pillars of computational reproducibility: bioinformatics and beyond.Briefings in bioinformatics · 2023Article
- Standards, dissemination, and best practices in systems biology.Current opinion in biotechnology · 2023Review
- Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility.Frontiers in systems biology · 2022Article
- Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective.NPJ systems biology and applications · 2021Article
- Article
- Computational Verification of Large Logical Models-Application to the Prediction of T Cell Response to Checkpoint Inhibitors.Frontiers in physiology · 2020Article
- Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders.Frontiers in psychiatry · 2019Review
- OpenWorm: overview and recent advances in integrative biological simulation ofPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Review
- Towards systematic, data-driven validation of a collaborative, multi-scale model ofPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
- Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data.Frontiers in neuroinformatics · 2018Article
- Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices.Cellular logistics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models.
Indexed as
Identifiers
What OpenQuestion holds
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.