Robust predictors for drug response of patients with acute myeloid leukemia.
리뷰
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
환자: acute myeloid leukemia (AML) underscores the critical need for accurate drug response prediction
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
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.
The significant heterogeneity in treatment responses among patients with acute myeloid leukemia (AML) underscores the critical need for accurate drug response prediction.
APA
Tercan B (2026). Robust predictors for drug response of patients with acute myeloid leukemia.. PloS one, 21(2), e0343422. https://doi.org/10.1371/journal.pone.0343422
MLA
Tercan B. "Robust predictors for drug response of patients with acute myeloid leukemia.." PloS one, vol. 21, no. 2, 2026, pp. e0343422.
PMID
41729921 ↗
Abstract 한글 요약
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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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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