ReviewJournal of biomolecular screening2014
Increasing the Content of High-Content Screening: An Overview.
Review in Journal of biomolecular screening, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 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
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Who cites it
78 citing papers in PubMed.
- Artificial Intelligence-Powered High-Content Analysis: Methodologies and Applications in Bioactive Compound Discovery from Natural Sources.Molecules (Basel, Switzerland) · 2026Review
- Review
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- Acquisition, quality control, and architecture of a large image dataset as a tool for in silico cell biological research.Materials today. Bio · 2025Article
- Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.Human genomics · 2025Review
- High-Throughput Methods for the Discovery of Small Molecule Modulators of Pancreatic Beta-Cell Function and Regeneration.Assay and drug development technologies · 2024Review
- Transfer learning for versatile and training free high content screening analyses.Scientific reports · 2023Article
- Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods.Biomedicines · 2023Article
- Effects of Immune Cell Heterogeneity and Protein Corona on the Cellular Association and Cytotoxicity of Gold Nanoparticles: A Single-Cell-Based, High-Dimensional Mass Cytometry Study.ACS nanoscience Au · 2023Article
- Unbiased single-cell morphology with self-supervised vision transformers.bioRxiv : the preprint server for biology · 2023Article
- In vitro to in vivo extrapolation and high-content imaging for simultaneous characterization of chemically induced liver steatosis and markers of hepatotoxicity.Archives of toxicology · 2023Article
- Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs).Frontiers in cell and developmental biology · 2023Article
- Predicting multipotency of human adult stem cells derived from various donors through deep learning.Scientific reports · 2022Article
- Small-molecule PROTAC mediates targeted protein degradation to treat STAT3-dependent epithelial cancer.JCI insight · 2022Article
- Single object profiles regression analysis (SOPRA): a novel method for analyzing high-content cell-based screens.BMC bioinformatics · 2022Article
- High throughput screening of mesenchymal stem cell lines using deep learning.Scientific reports · 2022Article
- An integrated pipeline for high-throughput screening and profiling of spheroids using simple live image analysis of frame to frame variations.Methods (San Diego, Calif.) · 2021Article
- Image-based profiling for drug discovery: due for a machine-learning upgrade?Nature reviews. Drug discovery · 2021Review
- High-content screening of Thai medicinal plants reveals Boesenbergia rotunda extract and its component Panduratin A as anti-SARS-CoV-2 agents.Scientific reports · 2020Article
- High-content, label-free analysis of proplatelet production from megakaryocytes.Journal of thrombosis and haemostasis : JTH · 2020Article
18 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising. In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60-80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information. We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.
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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.