ArticlePloS one2026
Robust predictors for drug response of patients with acute myeloid leukemia.
Article in PloS one, 2026. 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
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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
2 citing papers in PubMed.
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- p97 Inhibition Synergistically Enhances Hypomethylating Therapy through Targeting of PLK1 in Acute Myeloid Leukemia.Cancer research communications · 2026Article
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Authors and funding
1 author.
Funding
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Abstract
The significant heterogeneity in treatment responses among patients with acute myeloid leukemia (AML) underscores the critical need for accurate drug response prediction. We developed k-Top Scoring Pairs (kTSP) classifiers, ensemble methods that aggregate the relative expression of gene pairs. We compared their accuracy with that of state-of-the-art machine learning methods, linear and radial basis function support vector machines, random forest and elastic net regression classifiers for drug response prediction of patients with AML. Our results demonstrate that kTSP particularly outperforms other methods when the number of sensitive and resistant patients is imbalanced, a common challenge in clinical studies. Our approach is inherently robust to batch effects and uniquely suited for single-patient classification due to its rank-based methodology.
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Registered trials
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