TargetPrior: A miRNA-Signature Embedded Evolutionary Learning Framework for Prioritizing Drug Targets in Acute Myeloid Leukemia

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ID: 326658
2026
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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.
Reference Key
openalex_W7204435227 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ting‐Yu Chen, Shinn‐Ying Ho
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag635
URL
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag635/70818941/btag635.pdf
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