ArticleBMC bioinformatics2021
Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Advances and challenges in drug repurposing in precision therapeutics of colorectal cancer.World journal of gastrointestinal oncology · 2025Review
- The computationally guided design of selective targeters of multiple proteins (STaMPs) as a new opportunity for small molecule drug discovery.Frontiers in pharmacology · 2025Review
- Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning.Cognitive neurodynamics · 2023Article
- Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing.BMC bioinformatics · 2023Article
- Compound Danshen Dripping Pill inhibits hypercholesterolemia/atherosclerosis-induced heart failure in ApoE and LDLR dual deficient miceActa pharmaceutica Sinica. B · 2023Article
- Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.Journal of clinical medicine · 2023Review
- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundQuerying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach.
resultsIn this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs.
conclusionGPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.
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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 Socratic graph.