TargetPrior: a miRNA-signature embedded evolutionary learning framework for prioritizing drug targets in acute myeloid leukemia
TL;DR
TargetPrior is presented as a computational proof-of-concept for generating prioritized therapeutic hypotheses, rather than as a universal target-discovery solution, and showed stronger early-rank retrieval than network-only and statistical approaches.
Abstract
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).