ArticleAutophagy2020
DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in
Article in Autophagy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)Autophagy · 2021Guideline
- A deep learning and large language hybrid workflow for omics interpretation.Nature biomedical engineering · 2026Article
- Evolutionary diversification of the autophagy initiation complex: reduced Atg101 dependency and changes in Atg9 binding to Atg13.Autophagy · 2025Article
- Explainable AI to unveil cellular autophagy dynamics.PloS one · 2025Article
- Omic AI reveals new autophagy regulators from the Atg1 interactome inFrontiers in cell and developmental biology · 2025Article
- PunctaFinder: An algorithm for automated spot detection in fluorescence microscopy images.Molecular biology of the cell · 2024Article
- Atg45 is an autophagy receptor for glycogen, a non-preferred cargo of bulk autophagy in yeast.iScience · 2024Article
- Deep learning-based identification of spine growth potential on EOS radiographs.European radiology · 2024Article
- Automated quantification of vacuole fusion and lipophagy inAutophagy · 2024Article
- Modulating autophagy to treat diseases: A revisited review on in silico methods.Journal of advanced research · 2024Review
- Metrics reloaded: recommendations for image analysis validation.Nature methods · 2024Review
- CELLULAR, A Cell Autophagy Imaging Dataset.Scientific data · 2023Article
- Article
- Deep learning-based image processing in optical microscopy.Biophysical reviews · 2022Review
- Article
- DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology.Plant physiology · 2021Article
- Robust classification of cell cycle phase and biological feature extraction by image-based deep learning.Molecular biology of the cell · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (
Indexed as
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.