ArticleMolecular biology of the cell2017
A deep learning and novelty detection framework for rapid phenotyping in high-content screening.
Article in Molecular biology of the cell, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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
36 citing papers in PubMed.
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- High-content phenotyping reveals Golgi dynamics and their role in cell cycle regulation.The Journal of cell biology · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 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
- Article
- 3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data.Cell reports methods · 2023Article
- Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes.Science advances · 2022Article
- Systematic discovery of biomolecular condensate-specific protein phosphorylation.Nature chemical biology · 2022Article
- Precise measurement of nanoscopic septin ring structures with deep learning-assisted quantitative superresolution microscopy.Molecular biology of the cell · 2022Article
- Sister chromatid-sensitive Hi-C to map the conformation of replicated genomes.Nature protocols · 2022Review
- Unleashing high content screening in hit detection - Benchmarking AI workflows including novelty detection.Computational and structural biotechnology journal · 2022Article
- Number of necessary training examples for Neural Networks with different number of trainable parameters.Journal of pathology informatics · 2022Article
- LiveCellMiner: A new tool to analyze mitotic progression.PloS one · 2022Article
- Artificial intelligence unifies knowledge and actions in drug repositioning.Emerging topics in life sciences · 2021Review
- Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with
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.