ArticleNature communications2020
Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Automated kinetic-scheme-free sorting of single-molecule fluorescence events using a deep learning based hidden-state method.Nature communications · 2026Article
- Novel convolutional neural network for bacterial identification of confocal microscopic datasets.Scientific reports · 2026Article
- Multiplexed single-molecule characterization at the library scale.Nature protocols · 2026Review
- Foundation model for efficient biological discovery in single-molecule time traces.Nature methods · 2025Article
- Machine learning for accelerating discovery from single-molecule data.Nature methods · 2025Article
- Analytical techniques for nucleic acid and protein detection with single-molecule sensitivity.Experimental & molecular medicine · 2025Review
- Reaction Pathway Differentiation Enabled Fingerprinting Signal for Single Nucleotide Variant Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Pretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.The journal of physical chemistry. B · 2025Article
- Deep learning based local feature classification to automatically identify single molecule fluorescence events.Communications biology · 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
- Increasing the accuracy of single-molecule data analysis using tMAVEN.bioRxiv : the preprint server for biology · 2024Article
- Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures.Nature communications · 2023Article
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
- Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins.Nature methods · 2023Article
- Deep learning in single-molecule imaging and analysis: recent advances and prospects.Chemical science · 2022Review
- A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories.Nature communications · 2022Article
- Toward high-throughput oligomer detection and classification for early-stage aggregation of amyloidogenic protein.Frontiers in chemistry · 2022Article
- Unsupervised selection of optimal single-molecule time series idealization criterion.Biophysical journal · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we use deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholding. As a second application, the selector is used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. Finally, we show that AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.
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