ReviewFrontiers in molecular biosciences2021
Reconciling Simulations and Experiments With BICePs: A Review.
Review in Frontiers in molecular biosciences, 2021. 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
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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.
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.
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Who cites it
11 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Article
- Automatic forward model parameterization with Bayesian inference of conformational populations.APL machine learning · 2026Article
- Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations.ArXiv · 2025Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.Bioinformatics (Oxford, England) · 2024Article
- Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding.The journal of physical chemistry. B · 2023Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.bioRxiv : the preprint server for biology · 2023Article
- Folding@home: Achievements from over 20 years of citizen science herald the exascale era.Biophysical journal · 2023Review
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Folding@home: achievements from over twenty years of citizen science herald the exascale era.ArXiv · 2023Article
- Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution.Journal of chemical information and modeling · 2021Article
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
Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The Bayesian framework of BICePs enables population reweighting as a post-simulation processing step, with several advantages over existing methods, including the proper use of reference potentials, and the estimation of a Bayes factor-like quantity called the BICePs score for model selection. Here, we summarize the theory underlying this method in context with related algorithms, review the history of BICePs applications to date, and discuss current shortcomings along with future plans for improvement.
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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.