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Improving Phenotypic Measurements in High-Content Imaging Screens
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Correlation Alignment for Unsupervised Domain Adaptation
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Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms
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inferredcites →https://doi.org/10.1016/j.drudis.2012.10.010openalex-snapshot
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The why and how of phenotypic small-molecule screens
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Annotated high-throughput microscopy image sets for validation
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Quantitative high-throughput screening: A titration-based approach that efficiently identifies biological activities in large chemical libraries
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A multi-scale convolutional neural network for phenotyping high-content cellular images
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Automating Morphological Profiling with Generic Deep Convolutional Networks
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Learning Fine-Grained Image Similarity with Deep Ranking
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A Threshold Selection Method from Gray-Level Histograms
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Pipeline for illumination correction of images for high‐throughput microscopy
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Developing predictive assays: The phenotypic screening “rule of 3”
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High-Content Phenotypic Profiling of Drug Response Signatures across Distinct Cancer Cells
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Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment
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Increasing the Content of High-Content Screening: An Overview
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When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis
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arm: Data Analysis Using Regression and Multilevel/Hierarchical Models
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-34356-9_35openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-030-87589-3_58openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-08751-6_23openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-43993-3_65openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-032-09513-8_42openalex-snapshot
Scheduled Cross-Domain Multi-center DINO for Robust High-Content Screening Representation Learning
inferred← citeshttps://doi.org/10.1007/s42452-026-08748-1openalex-snapshot
AI-driven drug development: integrating computational design, 3D printing, and genomic medicine
inferred← citeshttps://doi.org/10.1016/j.cbpa.2021.04.001openalex-snapshot
Image-based cell phenotyping with deep learning
inferred← citeshttps://doi.org/10.1016/j.cels.2022.10.001openalex-snapshot
Morphology and gene expression profiling provide complementary information for mapping cell state
inferred← citeshttps://doi.org/10.1016/j.chembiol.2021.02.015openalex-snapshot
High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need
inferred← citeshttps://doi.org/10.1016/j.coisb.2018.05.004openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.csbj.2024.02.022openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.csbj.2025.02.020openalex-snapshot
uniDINO: Assay-independent feature extraction for fluorescence microscopy images
inferred← citeshttps://doi.org/10.1016/j.insi.2025.100049openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.isci.2024.110511openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.isci.2024.111434openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.media.2024.103123openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.medntd.2023.100276openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neunet.2020.09.007openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.slast.2021.10.015openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.taap.2025.117530openalex-snapshot
Cell morphology and gene expression: tracking changes and complementarity across time and cell lines
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inferred← citeshttps://doi.org/10.1038/s41467-024-55707-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41573-020-00117-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41587-024-02391-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41592-024-02185-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-025-88825-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41746-019-0131-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s42003-025-07766-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s42003-025-09500-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s42256-025-01156-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s44320-026-00197-7openalex-snapshot
Progress and new challenges in image-based profiling.
inferred← citeshttps://doi.org/10.1039/9781837676941-00209openalex-snapshot
Artificial Intelligence-based Morphological Profiling for Drug Discovery: Advancing Towards 4D Phenotypic Profiling
inferred← citeshttps://doi.org/10.1039/d3dd00060eopenalex-snapshot
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inferred← citeshttps://doi.org/10.1073/pnas.2002738117openalex-snapshot
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inferred← citeshttps://doi.org/10.1091/mbc.e21-11-0538openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbae284openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btab497openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btab853openalex-snapshot
BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations
inferred← citeshttps://doi.org/10.1101/2022.05.15.491989openalex-snapshot
High-content cellular screen image analysis benchmark study
inferred← citeshttps://doi.org/10.1101/2022.09.11.507500openalex-snapshot
In silico labeling enables kinetic myelination assay in brightfield
inferred← citeshttps://doi.org/10.1101/2022.10.14.512286openalex-snapshot
Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images
inferred← citeshttps://doi.org/10.1101/2022.10.24.512549openalex-snapshot
A scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data
inferred← citeshttps://doi.org/10.1101/2023.05.01.538999openalex-snapshot
Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation
inferred← citeshttps://doi.org/10.1101/2023.10.16.561085openalex-snapshot
Assessing the limits of zero-shot foundation models in single-cell biology
inferred← citeshttps://doi.org/10.1101/2024.01.04.574270openalex-snapshot
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inferred← citeshttps://doi.org/10.1101/2024.04.19.590313openalex-snapshot
Asymmetric Contrastive Objectives for Efficient Phenotypic Screening
inferred← citeshttps://doi.org/10.1101/227645openalex-snapshot
CytoGAN: Generative Modeling of Cell Images
inferred← citeshttps://doi.org/10.1101/725754openalex-snapshot
Metadata-Guided Visual Representation Learning for Biomedical Images
inferred← citeshttps://doi.org/10.1101/824862openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cvpr.2018.00970openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.01117openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cvprw59228.2023.00451openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iotais67227.2025.11282097openalex-snapshot
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inferred← citeshttps://doi.org/10.1126/sciadv.aba9338openalex-snapshot
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inferred← citeshttps://doi.org/10.1128/spectrum.01472-22openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/2472555218818756openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/2472555218820805openalex-snapshot
Evaluation of Machine Learning Classifiers to Predict Compound Mechanism of Action When Transferred across Distinct Cell Lines
inferred← citeshttps://doi.org/10.1177/2472555219857715openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12859-018-2087-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12936-026-06047-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13059-025-03574-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s44330-024-00014-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pcbi.1007348openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pcbi.1011323openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pcbi.1012463openalex-snapshot
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inferred← citeshttps://doi.org/10.14232/phd.11423openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/s25051350openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.1906.07282openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.1912.07661openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.2008.11673openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.2012.12175openalex-snapshot
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inferred← citeshttps://doi.org/10.70675/9cec2bcaz6d3ez4754zb225zf0beec41463dopenalex-snapshot
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inferred← citeshttps://doi.org/10.70675/b911c58fz372cz4c21zbe03z44cd28dbb87aopenalex-snapshot
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inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/1openalex-snapshot
Peer Review #1 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/2openalex-snapshot
Peer Review #2 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/3openalex-snapshot
Peer Review #3 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.2/reviews/1openalex-snapshot
Peer Review #1 of "Correcting nuisance variation using Wasserstein distance (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.2/reviews/3openalex-snapshot
Peer Review #3 of "Correcting nuisance variation using Wasserstein distance (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj.8594openalex-snapshot
Correcting nuisance variation using Wasserstein distance
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