ArticleG3 (Bethesda, Md.)2017
Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.
Article in G3 (Bethesda, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed, 154 citations in OpenAlex.
- Discovery and design of potent cell surface display elements.Nature biotechnology · 2026Article
- AI-enabled label free monitoring of TGFβ1-induced remodeling in iPSC-derived alveolar organoids.Materials today. Bio · 2026Article
- Three-tier framework for high-throughput biofabrication: Integrating 3D bioprinting, assistive platforms, and translational opportunities.Bioactive materials · 2026Review
- AIstain: Enhancing microglial phagocytosis analysis through deep learning.Cell reports methods · 2025Article
- A dual-branch deep learning network for circulating tumor cells classification.Journal of translational medicine · 2025Article
- Imaging Flow Cytometry as a Molecular Biology Tool: From Cell Morphology to Molecular Mechanisms.International journal of molecular sciences · 2025Review
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.Lab on a chip · 2024Article
- High-throughput assays to assess variant effects on disease.Disease models & mechanisms · 2024Article
- Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks.Scientific reports · 2024Article
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- High-Content Image-Based Screening and Deep Learning for the Detection of Anti-Inflammatory Drug Leads.Chembiochem : a European journal of chemical biology · 2024Article
- Transfer learning for versatile and training free high content screening analyses.Scientific reports · 2023Article
- CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer.Advances in neural information processing systems · 2023Article
- Article
- Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.Bioinformatics (Oxford, England) · 2023Article
- A review from biological mapping to computation-based subcellular localization.Molecular therapy. Nucleic acids · 2023Review
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.
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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.