ReviewAnnual review of biophysics2021
Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments.
Review in Annual review of biophysics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Multiple data set Bayesian analysis synergistically boosts ITC parameter precision.Biophysical journal · 2026Article
- Elevator mechanism dynamics in a sodium-coupled dicarboxylate transporter.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Quantitative analysis methods for free diffusion single-molecule FRET experiments.Current opinion in structural biology · 2025Review
- Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to isothermal titration calorimetry.bioRxiv : the preprint server for biology · 2025Article
- Probing actin-activated ATP turnover kinetics of human cardiac myosin II by single molecule fluorescence.Cytoskeleton (Hoboken, N.J.) · 2024Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.Biophysical journal · 2024Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.bioRxiv : the preprint server for biology · 2024Article
- Single-molecule tracking to determine the abundances and stoichiometries of freely-diffusing protein complexes in living cells: Past applications and future prospects.The Journal of chemical physics · 2023Review
- Entropic control of the free-energy landscape of an archetypal biomolecular machine.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories.Nature communications · 2022Article
- Bayesian machine learning analysis of single-molecule fluorescence colocalization images.eLife · 2022Article
- NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.Marine drugs · 2021Article
- KERA: analysis tool for multi-process, multi-state single-molecule data.Nucleic acids research · 2021Article
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3 authors.
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Abstract
Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.
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