ArticleAnalytical biochemistry2015
A histogram approach to the quality of fit in sedimentation velocity analyses.
Article in Analytical biochemistry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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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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.
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Who cites it
7 citing papers in PubMed.
- An automated interface for sedimentation velocity analysis in SEDFIT.PLoS computational biology · 2023Article
- SViMULATE: a computer program facilitating interactive, multi-mode simulation of analytical ultracentrifugation data.European biophysics journal : EBJ · 2023Article
- An automated interface for sedimentation velocity analysis in SEDFIT.bioRxiv : the preprint server for biology · 2023Article
- Enhanced Sample Handling for Analytical Ultracentrifugation with 3D-Printed Centerpieces.Analytical chemistry · 2019Article
- Returning Results: Let's Be Honest!Genetic testing and molecular biomarkers · 2017Article
- Sedimentation coefficient distributions of large particles.The Analyst · 2016Article
- Variable Field Analytical Ultracentrifugation: II. Gravitational Sweep Sedimentation Velocity.Biophysical journal · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The quality of fit of sedimentation velocity data is critical to judge the veracity of the sedimentation model and accuracy of the derived macromolecular parameters. Absolute statistical measures are usually complicated by the presence of characteristic systematic errors and run-to-run variation in the stochastic noise of data acquisition. We present a new graphical approach to visualize systematic deviations between data and model in the form of a histogram of residuals. In comparison with the ideally expected Gaussian distribution, it can provide a robust measure of fit quality and be used to flag poor models.
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Registered trials
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