ArticleCell2018
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.
Article in Cell, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06245694 (Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits), which is not on this map. Cited by 293 papers.
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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.
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits
Who cites it
293 citing papers in PubMed.
- An Integrated Workflow for Culturing and AI-Based Profiling of Mouse Intestinal Epithelium.Methods in molecular biology (Clifton, N.J.) · 2027Article
- 'In silico labelling': Exploring the biological landscape at the nanoscale using intelligent multimodal optical microscopy.Journal of microscopy · 2026Review
- Quantitative imaging of dynamic processes in intestinal organoids.Nature protocols · 2026Review
- Review
- Urinary Cell-Free DNA detection platform for bacterial cystitis diagnosis via spot-based machine learning.Journal of advanced research · 2026Article
- Advancing Cerebro-cerebellar Network Imaging with Ultra-high Field (7T) MRI: Progress and Future Challenges.Cerebellum (London, England) · 2026Review
- Machine Learning Models of Phase Contrast Images Predict Efficiency of Human Pluripotent Stem Cell Differentiation to Cardiomyocytes.Biotechnology and bioengineering · 2026Article
- ST2HE: enhancing spatial transcriptomics interpretability via virtual staining for histological annotation.Briefings in bioinformatics · 2026Article
- Deep Learning-Powered Scalable Cancer Organ Chip for Cancer Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modeling glioblastoma relapse in vitro: a critical journey from 2D models to organ-on-chip alternatives.NPJ precision oncology · 2026Review
- A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.bioRxiv : the preprint server for biology · 2026Article
- Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification.bioRxiv : the preprint server for biology · 2026Article
- Generative AI for misalignment-resistant virtual staining to accelerate histopathology workflows.Nature communications · 2026Article
- 2D Multimodal Image Collection for Fluorescence Prediction from Transmitted Light Microscopy.Scientific data · 2026Article
- Deep learning-based in silico labeling for analyzing morphological features of MSCs to predict immunomodulatory capacity.Communications biology · 2026Article
- Temporal variance mapping with machine learning for label-free 3D chromatin imaging using optical interferometric microscopy.Biomedical optics express · 2026Article
- Regularized Single-Cell Imaging Enables Generalizable AI Models for Stain-Free Cell Viability Screening.Small methods · 2026Article
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Cell context-dependent in silico organelle localization in label-free microscopy images.Nature methods · 2026Article
- Smart hybrid microscopy for cell-friendly detection of rare events.Nature communications · 2026Article
233 more citing papers are in PubMed but not listed here.
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Authors and funding
18 authors.
Funding
Abstract
Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.
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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.