ReviewBiophysical journal2017
An Introduction to Infinite HMMs for Single-Molecule Data Analysis.
Review in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
56 citing papers in PubMed.
- Resolving fluorescently labeled species using highly multiplexed spectral FLIM.Scientific reports · 2026Article
- Substrate-interacting pore loops of two ATPase subunits determine the degradation efficiency of the 26S proteasome.Nature communications · 2026Article
- Hidden Markov model analysis of fluorescence blinking in fluorescently labeled DNA.Scientific reports · 2026Article
- A cautious user's guide in applying HMMs to physical systems.The Journal of chemical physics · 2025Article
- Unmasking complex kinetics in viral entry by inferring hypoexponential models.Biophysical journal · 2025Article
- Quantitative Assessment of Biological Dynamics with Aggregate Data.Bulletin of mathematical biology · 2025Article
- Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting.Biophysical journal · 2025Article
- Bayesian Nonparametrics for FRET using Realistic Integrative Detectors.bioRxiv : the preprint server for biology · 2025Article
- Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting.bioRxiv : the preprint server for biology · 2025Article
- Bayesian Inference of Binding Kinetics from Fluorescence Time Series.The journal of physical chemistry. B · 2025Article
- Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.bioRxiv : the preprint server for biology · 2025Article
- Cholesterol inhibits assembly and oncogenic activation of the EphA2 receptor.Communications biology · 2025Article
- Super-photostable organic dye for long-term live-cell single-protein imaging.Nature methods · 2025Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.Biophysical journal · 2024Article
- An accurate probabilistic step finder for time-series analysis.Biophysical journal · 2024Article
- HS-AFM single-molecule structural biology uncovers basis of transporter wanderlust kinetics.Nature structural & molecular biology · 2024Article
- Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on hidden Markov model.Scientific reports · 2024Article
- Cholesterol inhibits assembly and activation of the EphA2 receptor.bioRxiv : the preprint server for biology · 2024Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.bioRxiv : the preprint server for biology · 2024Article
- Building Fluorescence Lifetime Maps Photon-by-Photon by Leveraging Spatial Correlations.ACS photonics · 2023Article
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
The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. Although the current literature on the iHMM is primarily intended for audiences in statistics, the idea is powerful and the iHMM's breadth in applicability outside machine learning and data science warrants a careful exposition. Here, we explain the key ideas underlying the iHMM, with a special emphasis on implementation, and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.
Indexed as
Identifiers
What OpenQuestion holds
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.