ArticleScientific reports2020
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 75 citations in OpenAlex.
- Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.Functional & integrative genomics · 2026Review
- CellPHIE: Integrating Pathway Discovery With Pooled Profiling of Perturbations Uncovers Pathways of Huntington's Disease, Including Genetic Modifiers of Neuronal Development and Morphology.bioRxiv : the preprint server for biology · 2025Article
- A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.PLoS computational biology · 2024Article
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- HD_BPMDS: a curated binary pattern multitarget dataset of Huntington's disease-targeting agents.Journal of cheminformatics · 2023Article
- The hitchhikers' guide to RNA sequencing and functional analysis.Briefings in bioinformatics · 2023Article
- High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations.Nature methods · 2022Article
- A Novel Huntington's Disease Assessment Platform to Support Future Drug Discovery and Development.International journal of molecular sciences · 2022Article
- Integrative modeling of the cell.Acta biochimica et biophysica Sinica · 2022Review
- Expanding the search for small-molecule antibacterials by multidimensional profiling.Nature chemical biology · 2022Review
- From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways.Integrative biology : quantitative biosciences from nano to macro · 2022Article
- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
- Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells.Expert review of proteomics · 2021Review
- Translational precision medicine: an industry perspective.Journal of translational medicine · 2021Review
- How artificial intelligence might disrupt diagnostics in hematology in the near future.Oncogene · 2021Review
- The remodel of the "central dogma": a metabolomics interaction perspective.Metabolomics : Official journal of the Metabolomic Society · 2021Review
- Discovering the landscape of protein modifications.Molecular cell · 2021Review
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases.
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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.