Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

DeepFoci: Deep learning-based algorithm for fast automatic analysis of DNA double-strand break ionizing radiation-induced foci

1 Jan 2021

Abstract excerpt

DNA double-strand breaks (DSBs), marked by ionizing radiation-induced (repair) foci (IRIFs), are the most serious DNA lesions and are dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold-standard method in radiation biodosimetry and allows research on DSB induction and repair at the molecular and single-cell levels. In this study, we introduce DeepFoci...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer-related Molecular Pathways
Share to DNA Repair Mechanisms
Share to Radiation Therapy and Dosimetry

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 34976305
DOI: 10.1016/j.csbj.2021.11.019

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 9 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

DeepFoci: Deep learning-based algorithm for fast automatic analysis of DNA double-strand break ionizing radiation-induced fociPMID 34976305Read ↗

micronuclAI: Automated quantification of micronuclei for assessment of chromosomal instabilitySemantic proximity Read ↗

Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer typesSemantic proximity Read ↗

Characterizing the DNA Damage Response by Cell Tracking Algorithms and Cell Features Classification Using High-Content Time-Lapse AnalysisSemantic proximity Read ↗

CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal ModelsSemantic proximity Read ↗

FiberAI: A Deep Learning model for automated analysis of nascent DNA FibersSemantic proximity Read ↗

AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosisSemantic proximity Read ↗

Recruitment of 53BP1 Proteins for DNA Repair and Persistence of Repair Clusters Differ for Cell Types as Detected by Single Molecule Localization MicroscopySemantic proximity Read ↗

Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide imagesSemantic proximity Read ↗

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samplesSemantic proximity Read ↗

Improved identification of DNA double strand breaks: γ-H2AX-epitope visualization by confocal microscopy and 3D reconstructed imagesSemantic proximity Read ↗

Automated detection of the HER2 gene amplification status in Fluorescence <i>in situ</i> hybridization images for the diagnostics of cancer tissuesSemantic proximity Read ↗

Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometrySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.