Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images
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Summary
A computational pipeline is presented to distinguish the true signal of fluorescently labeled molecules from background fluorescence and noise in fluorescence microscopy images and makes it possible to estimate the error introduced by image classification.
- Type
- article
- Published
- 2015-03-27
- Cited by
- 24
- References
- 48
- Access
- Open access
- OpenAlex
- https://openalex.org/W25880543
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:12690864
Keywords
Welfare, Political science, Law
References
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- Visualization of single RNA transcripts in situ.
Cited by
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- The parameter sensitivity of random forests
- Single-molecule techniques in biophysics: a review of the progress in methods and applications
- Yeast silencing factor Sir4 and a subset of nucleoporins form a complex distinct from nuclear pore complexes
- An automated workflow for quantifying RNA transcripts in individual cells in large data-sets
- DeepFRET: Rapid and automated single molecule FRET data classification using deep learning
- DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning
- Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
- Decoding Optical Data with Machine Learning
- Direct kinetic fingerprinting for high-accuracy single-molecule counting of diverse disease biomarkers
- Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night
- A GATA factor radiation in Caenorhabditis rewired the endoderm specification network
- Single-molecule FISH in C. elegans embryos reveals early embryonic expression dynamics of par-2 , lgl-1 and chin-1 and possible differences between hyper-diverged strains
- Decoding Optical Spectra with Neural Networks to Monitor the Elimination of Carbon Nanoagents from the Body
- Diversification of small RNA pathways underlies germline RNA interference incompetence in wild Caenorhabditis elegans strains
- Combining Visual Saliency Methods and Sparse Keypoint Annotations to Create Object Representations for Providently Detecting Vehicles at Night
- Characterizing the Spatial Distribution of Dendritic RNA at Single Molecule Resolution
- Identification of germline chromatin modifying factors that influence zygotic transcription activation in C. elegans
- Yeast silencing factor Sir4 and a subset of nucleoporins form a complex distinct from nuclear pore complexes
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