ArticleeLife2020
DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed, 80 citations in OpenAlex.
- Automated kinetic-scheme-free sorting of single-molecule fluorescence events using a deep learning based hidden-state method.Nature communications · 2026Article
- ATP-driven conformational dynamics reveal hidden intermediates in a heterodimeric ABC transporter.eLife · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- Single-molecule dynamics reveal ATP binding alone powers substrate translocation by an ABC transporter.Nature communications · 2026Article
- Multiplexed single-molecule characterization at the library scale.Nature protocols · 2026Review
- The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives.Journal of molecular biology · 2025Review
- Foundation model for efficient biological discovery in single-molecule time traces.Nature methods · 2025Article
- Bayesian Nonparametrics for FRET using Realistic Integrative Detectors.bioRxiv : the preprint server for biology · 2025Article
- Observation of SAM-VI Riboswitch Dynamics Using Single-Molecule FRET.Biomolecules · 2025Article
- Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function.Nature methods · 2025Article
- Pretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.The journal of physical chemistry. B · 2025Article
- Article
- Deep learning based local feature classification to automatically identify single molecule fluorescence events.Communications biology · 2024Article
- Divergent molecular assembly and catalytic mechanisms between bacterial and archaeal RNase P in pre-tRNA cleavage.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep-LASI, single-molecule data analysis software.Biophysical journal · 2024Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.Biophysical journal · 2024Article
- An optofluidic antenna for enhancing the sensitivity of single-emitter measurements.Nature communications · 2024Article
- SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis.Nature communications · 2024Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.
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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.