ArticleMolecular therapy. Oncology2026
Integrating multi-omics analysis and machine learning to refine molecular subtypes and prognostic assessment of lower-grade glioma.
Article in Molecular therapy. Oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Precision medicine meets artificial intelligence: A new prognostic model for lower-grade glioma.Molecular therapy. Oncology · 2026Article
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8 authors.
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Abstract
Lower-grade glioma (LGG) is a highly heterogeneous disease, making accurate prognosis prediction and the development of precise, personalized treatment plans for patients challenging. The multi-omics data from patients with LGG were analyzed using 10 clustering algorithms to identify subgroups at very high resolution. Ten machine learning algorithms were subsequently integrated to develop a robust artificial intelligence model (AIM). We identified two cancer subtypes (CSs) linked to prognosis through multi-omics clustering. After screening 46 hub genes, we integrated 10 machine learning algorithms into 117 combinations to select the AIM with the highest average C-index. The gradient boosting machine (GBM)-based AIM outperformed previous prognostic signatures in nearly all cohorts. Patients in the low-AIM group had a better prognosis and greater sensitivity to immunotherapy, whereas those in the high-AIM group had a poorer prognosis and lower immunotherapy sensitivity. However, MG132 showed promise as a potential therapeutic agent.
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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.