ArticleCold Spring Harbor symposia on quantitative biology2017
SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging Approaches.
Article in Cold Spring Harbor symposia on quantitative biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- U-FISH: a fluorescent spot detector for imaging-based spatial-omics analysis and AI-assisted FISH diagnosis.Genome biology · 2025Article
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- High-throughput image processing software for the study of nuclear architecture and gene expression.Scientific reports · 2024Article
- Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.Cell systems · 2024Article
- Computational approach for counting of SISH amplification signals for HER2 status assessment.PeerJ. Computer science · 2024Article
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing.Biophysics reviews · 2023Review
- Pooled image-base screening of mitochondria with microraft isolation distinguishes pathogenic mitofusin 2 mutations.Communications biology · 2022Article
- Nuclear position modulates long-range chromatin interactions.PLoS genetics · 2022Article
- Large-scale mapping of positional changes of hypoxia-responsive genes upon activation.Molecular biology of the cell · 2022Article
- MYC amplifies gene expression through global changes in transcription factor dynamics.Cell reports · 2022Article
- DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images.Biological imaging · 2022Article
- The 3D Genome: From Structure to Function.International journal of molecular sciences · 2021Review
- A high-throughput DNA FISH protocol to visualize genome regions in human cells.STAR protocols · 2021Article
- deepBlink: threshold-independent detection and localization of diffraction-limited spots.Nucleic acids research · 2021Article
- Integration of the ImageJ Ecosystem in the KNIME Analytics Platform.Frontiers in computer science · 2020Article
- Pinpointing Cell Identity in Time and Space.Frontiers in molecular biosciences · 2020Article
- Transcriptional Bursting and Co-bursting Regulation by Steroid Hormone Release Pattern and Transcription Factor Mobility.Molecular cell · 2019Article
- Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
DNA fluorescence in situ hybridization (FISH) is the technique of choice to map the position of genomic loci in three-dimensional (3D) space at the single allele level in the cell nucleus. High-throughput DNA FISH methods have recently been developed using complex libraries of fluorescently labeled synthetic oligonucleotides and automated fluorescence microscopy, enabling large-scale interrogation of genomic organization. Although the FISH signals generated by high-throughput methods can, in principle, be analyzed by traditional spot-detection algorithms, these approaches require user intervention to optimize each interrogated genomic locus, making analysis of tens or hundreds of genomic loci in a single experiment prohibitive. We report here the design and testing of two separate machine learning-based workflows for FISH signal detection in a high-throughput format. The two methods rely on random forest (RF) classification or convolutional neural networks (CNNs), respectively. Both workflows detect DNA FISH signals with high accuracy in three separate fluorescence microscopy channels for tens of independent genomic loci, without the need for manual parameter value setting on a per locus basis. In particular, the CNN workflow, which we named SpotLearn, is highly efficient and accurate in the detection of DNA FISH signals with low signal-to-noise ratio (SNR). We suggest that SpotLearn will be useful to accurately and robustly detect diverse DNA FISH signals in a high-throughput fashion, enabling the visualization and positioning of hundreds of genomic loci in a single experiment.
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.