ArticleMolecular systems biology2017
Automated analysis of high-content microscopy data with deep learning.
Article in Molecular systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers.
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Who cites it
113 citing papers in PubMed.
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- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
- Persistent homology-based segmentation tool for membrane images.Cell reports methods · 2026Article
- A comprehensive overview of yeast libraries and their role in advancing cell biology.The FEBS journal · 2026Review
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- Unsupervised Variational-Autoencoder-Based Analysis of Morphological Representations in Magnetic-Nanoparticle-Treated Macrophages.Bioengineering (Basel, Switzerland) · 2026Article
- Dynamic Organellar Mapping in yeast reveals extensive protein localization changes during ER stress.Nature communications · 2025Article
- ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.BMC genomics · 2025Article
- Single-cell imaging of protein dynamics of paralogs reveals sources of gene retention.iScience · 2025Article
- Microscope Upcycling: Transforming legacy microscopes into automated cloud-integrated imaging systems.HardwareX · 2025Article
- uniDINO: Assay-independent feature extraction for fluorescence microscopy images.Computational and structural biotechnology journal · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.Nature communications · 2024Article
- Multi-omic analysis reveals genes and proteins integral to bioactivity of Echinochrome A isolated from the waste stream of the sea urchin industry in Aotearoa New Zealand.Food science & nutrition · 2024Article
- K29-linked free polyubiquitin chains affect ribosome biogenesis and direct ribosomal proteins to the intranuclear quality control compartment.Molecular cell · 2024Article
- High-resolution spatiotemporal mapping: a comprehensive view of eukaryotic cell cycle proteome dynamics.Signal transduction and targeted therapy · 2024Article
- Expanding TheCellVision.org: a central repository for visualizing and mining high-content cell imaging projects.Genetics · 2024Article
- PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.Molecular systems biology · 2024Article
- Automated quantification of vacuole fusion and lipophagy inAutophagy · 2024Article
53 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data.
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Registered trials
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