ArticleBiophysical journal2018
Variational Algorithms for Analyzing Noisy Multistate Diffusion Trajectories.
Article in Biophysical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- Detecting directed motion and confinement in single-particle trajectories using hidden variables.eLife · 2026Article
- Revealing plasma membrane protein dynamics in living plant cells with single-molecule tracking.Journal of experimental botany · 2025Article
- Dynamic binding of the bacterial chaperone Trigger factor to translating ribosomes inProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.Biophysical journal · 2024Article
- Robust Quantification of Live-Cell Single-Molecule Tracking Data for Fluorophores with Different Photophysical Properties.The journal of physical chemistry. B · 2024Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.bioRxiv : the preprint server for biology · 2024Article
- ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks.The Journal of cell biology · 2023Article
- Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data.Molecular biology of the cell · 2022Article
- Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments.Annual review of biophysics · 2021Review
- Fundamentals of the logarithmic measure for revealing multimodal diffusion.Biophysical journal · 2021Article
- Extracting Transition Rates in Particle Tracking Using Analytical Diffusion Distribution Analysis.Biophysical journal · 2020Article
- High-throughput, single-particle tracking reveals nested membrane domains that dictate KRaseLife · 2019Article
- An automated Bayesian pipeline for rapid analysis of single-molecule binding data.Nature communications · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-particle tracking offers a noninvasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise sources are needed to realize the full quantitative potential of the method. We report algorithms for hidden Markov-based analysis of single-particle tracking data, which incorporate most sources of experimental noise, including heterogeneous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speedups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single-particle tracking data.
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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.