ArticleActa crystallographica. Section A, Foundations and advances2016
Estimating the difference between structure-factor amplitudes using multivariate Bayesian inference.
Article in Acta crystallographica. Section A, Foundations and advances, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Bayesian machine learning improves single-wavelength anomalous diffraction phasing.Acta crystallographica. Section A, Foundations and advances · 2019Article
- Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest.Scientific reports · 2019Article
- Asymmetry in serial femtosecond crystallography data.Acta crystallographica. Section A, Foundations and advances · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In experimental research referencing two or more measurements to one another is a powerful tool to reduce the effect of systematic errors between different sets of measurements. The interesting quantity is usually derived from two measurements on the same sample under different conditions. While an elaborate experimental design is essential for improving the estimate, the data analysis should also maximally exploit the covariance between the measurements. In X-ray crystallography the difference between structure-factor amplitudes carries important information to solve experimental phasing problems or to determine time-dependent structural changes in pump-probe experiments. Here a multivariate Bayesian method was used to analyse intensity measurement pairs to determine their underlying structure-factor amplitudes and their differences. The posterior distribution of the model parameter was approximated with a Markov chain Monte Carlo algorithm. The described merging method is shown to be especially advantageous when systematic and random errors result in recording negative intensity measurements.
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.