ArticleOmics : a journal of integrative biology2022
A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression.
Article in Omics : a journal of integrative biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.Progress in biophysics and molecular biology · 2023Pooled it
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- Review
- A hybrid unsupervised methodology on artificial intelligence filtering for automatically processing cellular DNA-encoded library (DEL) datasets.Bioinformatics (Oxford, England) · 2026Article
- Drug Discovery from Gene Expression/Gene Regulation.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Recent advances in drug repurposing for cancer immunomodulation emerging strategies, mechanistic insights, and clinical translation.Frontiers in oncology · 2026Review
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Natural products proposed for the management of Huntington's disease (HD): a comprehensive review.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Artificial intelligence applications in rare and intractable diseases: Advances, challenges, and future directions.Intractable & rare diseases research · 2025Article
- Characterizing diseases using genetic and clinical variables: A data analytics approach.Quantitative biology (Beijing, China) · 2024Article
- Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases.Biology · 2024Article
- Data-driven normative values based on generative manifold learning for quantitative MRI.Scientific reports · 2024Article
- The use of artificial intelligence in the treatment of rare diseases: A scoping review.Intractable & rare diseases research · 2024Article
- Artificial intelligence in drug repurposing for rare diseases: a mini-review.Frontiers in medicine · 2024Review
- Article
- Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer.Cancer informatics · 2023Article
- Unlocking therapeutic potential: integration of drug repurposing and immunotherapy for various disease targeting.American journal of translational research · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by
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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.