ArticleScience advances2020
A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
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.
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Who cites it
27 citing papers in PubMed, 55 citations in OpenAlex.
- GID/CTLH E3 ligase complex control cell fate programs for sexual development of Plasmodium falciparum.Nature communications · 2026Article
- Review
- Deep learning enhanced deciphering of brain activity maps for discovery of therapeutics for brain disorders.iScience · 2025Article
- A highly efficient, scalable pipeline for fixed feature extraction from large-scale high-content imaging screens.iScience · 2024Article
- An adaptable, fit-for-purpose screening approach with high-throughput capability to determine speed of action and stage specificity of anti-malarial compounds.Antimicrobial agents and chemotherapy · 2024Article
- Review
- Leveraging computational tools to combat malaria: assessment and development of new therapeutics.Journal of cheminformatics · 2024Review
- Next Generation Chemiluminescent Probes for Antimalarial Drug Discovery.ACS infectious diseases · 2024Article
- PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2024Article
- Learning representations for image-based profiling of perturbations.Nature communications · 2024Article
- Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving.Journal of chemical education · 2024Article
- Genetic validation ofeLife · 2023Article
- Machine learning-based phenotypic imaging to characterise the targetable biology of Plasmodium falciparum male gametocytes for the development of transmission-blocking antimalarials.PLoS pathogens · 2023Article
- Plasmodium falciparum formins are essential for invasion and sexual stage development.Communications biology · 2023Article
- High-content imaging as a tool to quantify and characterize malaria parasites.Cell reports methods · 2023Article
- Leveraging artificial intelligence in the fight against infectious diseases.Science (New York, N.Y.) · 2023Review
- Strengthening malaria microscopy using artificial intelligence-based approaches in India.The Lancet regional health. Southeast Asia · 2022Article
- Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.Nature communications · 2022Article
- Live-cell fluorescence imaging of microgametogenesis in the human malaria parasite Plasmodium falciparum.PLoS pathogens · 2022Article
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.
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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.