ArticleJournal of the American Statistical Association2016
Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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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
7 citing papers in PubMed.
- Single-trajectory Bayesian modeling reveals multi-state diffusion of the MSH sliding clamp.NPJ systems biology and applications · 2026Article
- Simulating Freely Diffusing Single-Molecule FRET Data with Consideration of Protein Conformational Dynamics.The journal of physical chemistry. B · 2026Article
- Panel Data Analysis via Mechanistic Models.Journal of the American Statistical Association · 2019Article
- Bayesian-Estimated Hierarchical HMMs Enable Robust Analysis of Single-Molecule Kinetic Heterogeneity.Biophysical journal · 2019Article
- Article
- Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.PLoS computational biology · 2016Article
- Single-Molecule Analysis beyond Dwell Times: Demonstration and Assessment in and out of Equilibrium.Biophysical journal · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process.
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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.