ReviewThe Journal of cell biology2024
AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.
Review in The Journal of cell biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- A computational super-resolution framework for multidimensional fluorescence imaging.Science advances · 2026Article
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- Subcellular visualization and quantification of cyanotoxin synthesis in cyanobacteria reveals distinct compartmentation.Scientific reports · 2026Article
- Dynamic collaboration between mitochondria and organelles: mechanisms, functions, and disease implications.Apoptosis : an international journal on programmed cell death · 2026Review
- Towards improved particle reconstruction for single-molecule localization microscopy using geometric deep learning.Bioinformatics advances · 2026Article
- ClusterNet: Classifying Single-Molecule Localization Microscopy Datasets with Graph-Based Deep Learning of Supracluster Structure.Small science · 2025Article
- An update on recent advances in fluorescent materials for fluorescence molecular imaging: a review.RSC advances · 2025Review
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- The triple code model for advancing research in rare and undiagnosed diseases beyond the base pairs.Epigenomics · 2025Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Scaffolds and the scaffolding domain: an alternative paradigm for caveolin-1 signaling.Biochemical Society transactions · 2024Review
- Comparative Analysis of SPLICS and MCS-DETECT for Detecting Mitochondria-ER Contact Sites (MERCs).Contact (Thousand Oaks (Ventura County, Calif.))Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers tremendous potential for the discovery of new biology, that, by definition, is not known and lacks ground truth. Herein, we describe the application of weakly supervised paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.
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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.