ArticleRSC advances2025
Integrative machine learning-guided
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A molecular modeling and machine learning-based approach for the identification of potential human ALOX15 inhibitors.Journal of molecular modeling · 2026Article
- Targeting IDH-driven Oncometabolism in glioblastoma: a computational approach for the identification of potential inhibitors.Frontiers in bioinformatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutations in Isocitrate Dehydrogenase 1 (IDH1) are found in over 80% of WHO grade II/III gliomas. These mutations, specifically R132H, confer a neomorphic activity that converts α-ketoglutarate (α-KG) into the oncometabolite 2-hydroxyglutarate (2HG), a key driver in glioma pathogenesis. While the therapeutic potential of targeting mutant IDH1 (MT-IDH1) is established, the discovery of novel and selective inhibitors remains a priority. Leveraging the growing availability of pharmacological data, we developed regression-based ML models to predict pIC
Identifiers
What Socratic 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 Socratic graph.