ArticleNature communications2019
Capturing single-cell heterogeneity via data fusion improves image-based profiling.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Mirror Effect of Parvalbumin and Connexin 43 Expression in the Acute and Subacute Phases After Penetrating Traumatic Brain Injury Reveals a Non-Canonical Interaction.Molecules (Basel, Switzerland) · 2026Article
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.PLoS computational biology · 2024Article
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.bioRxiv : the preprint server for biology · 2024Article
- Learning representations for image-based profiling of perturbations.Nature communications · 2024Article
- Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis.Chemical research in toxicology · 2023Article
- A global genetic interaction network by single-cell imaging and machine learning.Cell systems · 2023Article
- Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.iScience · 2022Article
- High-throughput functional annotation of natural products by integrated activity profiling.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Discrete protein metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions.Micron (Oxford, England : 1993) · 2022Article
- Article
- Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.PLoS computational biology · 2021Article
- Cell morphology-based machine learning models for human cell state classification.NPJ systems biology and applications · 2021Article
- Image-based profiling for drug discovery: due for a machine-learning upgrade?Nature reviews. Drug discovery · 2021Review
- Autonomous adaptive data acquisition for scanning hyperspectral imaging.Communications biology · 2020Article
- A Gene Expression High-Throughput Screen (GE-HTS) for Coordinated Detection of Functionally Similar Effectors in Cancer.Cancers · 2020Review
- Combining Qdot Nanotechnology and DNA Nanotechnology for Sensitive Single-Cell Imaging.Advanced materials (Deerfield Beach, Fla.) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features' dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound's mechanism of action (MoA) and a gene's pathway.
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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.