ArticleActa crystallographica. Section D, Structural biology2020
Measuring and using information gained by observing diffraction data.
Article in Acta crystallographica. Section D, Structural biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Robust error calibration for serial crystallography.Acta crystallographica. Section D, Structural biology · 2025Article
- Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models.IEEE transactions on artificial intelligence · 2025Article
- Useful experimental aspects of small-wedge synchrotron crystallography for accurate structure analysis of protein molecules.Acta crystallographica. Section D, Structural biology · 2025Article
- ARCIMBOLDO at low resolution: Verification for coiled coils and globular proteins.Protein science : a publication of the Protein Society · 2024Article
- Fragment-based determination of a proteinase K structure from MicroED data using ARCIMBOLDO_SHREDDER.Acta crystallographica. Section D, Structural biology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The information gained by making a measurement, termed the Kullback-Leibler divergence, assesses how much more precisely the true quantity is known after the measurement was made (the posterior probability distribution) than before (the prior probability distribution). It provides an upper bound for the contribution that an observation can make to the total likelihood score in likelihood-based crystallographic algorithms. This makes information gain a natural criterion for deciding which data can legitimately be omitted from likelihood calculations. Many existing methods use an approximation for the effects of measurement error that breaks down for very weak and poorly measured data. For such methods a different (higher) information threshold is appropriate compared with methods that account well for even large measurement errors. Concerns are raised about a current trend to deposit data that have been corrected for anisotropy, sharpened and pruned without including the original unaltered measurements. If not checked, this trend will have serious consequences for the reuse of deposited data by those who hope to repeat calculations using improved new methods.
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