TargetPrior: A miRNA-Signature Embedded Evolutionary Learning Framework for Prioritizing Drug Targets in Acute Myeloid Leukemia.
TargetPrior is a computational framework that uses stability-selected relapse-associated miRNA signatures and literature-curated miRNA–gene networks to prioritize candidate drug targets in childhood acute myeloid leukemia, with benchmarking and independent dataset analysis supporting its ranking approach.
Open original publication →What the AI sees
TargetPrior is a computational framework that uses stability-selected relapse-associated miRNA signatures and literature-curated miRNA–gene networks to prioritize candidate drug targets in childhood acute myeloid leukemia, with benchmarking and independent dataset analysis supporting its ranking approach.
Research significance
The record supports TargetPrior as a hypothesis-generation and target-ranking tool; it remains an inference that its prioritized genes will identify effective therapies, improve relapse outcomes, or guide treatment selection because no functional target validation, drug testing, or clinical evaluation is reported.
Source abstract
MOTIVATION: Prioritizing therapeutic targets from high-dimensional transcriptomic profiles is hindered by the underdetermined nature of the p ≫ n setting. While miRNA signatures can inform target prioritization, conventional accuracy-driven methods may yield unstable predictive signatures, reducing downstream network reliability and topology-guided candidate ranking. RESULTS: We propose TargetPrior, a stability-aware evolutionary learning framework in which EL-CAML derives reproducible miRNA anchors from relapse-associated transcriptomic variation for candidate target prioritization. In childhood acute myeloid leukemia (CAML), EL-CAML identifies a parsimonious 18-miRNA continuous relapse-risk signature and 10 complementary stability-supported biomarkers, yielding 28 miRNAs for literature-curated miRNA-gene network construction. Repeated perturbation analysis supported the stability of high-frequency miRNAs, while analysis of the independent GSE196886 cell-sorted small RNA-seq dataset identified cell-population-specific expression differences. Benchmarking against an expanded set of clinically and biologically supported AML target references showed stronger early-rank retrieval than network-only and statistical approaches. TargetPrior is presented as a computational proof-of-concept for generating prioritized therapeutic hypotheses, rather than as a universal target-discovery solution. AVAILABILITY: Code is available at: https://github.com/NYCU-ICLAB/TargetPrior and archived on Zenodo (DOI: 10.5281/zenodo.20394263). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.