Benchmarking miRNA Target Prediction Methods
Abstract
Background: MicroRNAs (miRNAs) regulate gene expression by binding to target mRNAs. Although many tools predict miRNA-target interactions, predictions show limited concordance, and benchmarks relying on curated databases may be biased by overlap with tool-development data. Methods: We benchmarked seven tools (TargetScan, miRDB, miRmap, DIANA-microT, RNA22, miRWalk, and mirDIP) across approximately 59.7 million candidate miRNA-mRNA pairs. Each pair received an evidence-graded reference score (0-3) combining validated interactions from miRTarBase and TarBase with expression-derived evidence from paired miRNA-seq and mRNA-seq data in an induced pluripotent stem cell (iPSC) cohort of 22q11.2 deletion syndrome and control lines. Performance was evaluated using Type I error, statistical power, and false discovery proportion (FDP). Robustness was assessed by increasing reference-score stringency (truth signal) and including progressively lower-confidence predictions (model signal). Optimal cutoffs maximized Youden's J statistic. Results: Rankings were largely consistent across settings. TargetScan and miRDB maintained Type I error rates below 0.05, with TargetScan showing the lowest FDP, but both had limited power. mirDIP achieved the highest power at the cost of the highest Type I error rate. At optimal cutoffs, mirDIP best balanced sensitivity and specificity (Youden's J = 0.566), followed by DIANA-microT (J = 0.481); miRWalk and RNA22 had FDPs near the random-prediction baseline. No tool was optimal across all metrics; tool and cutoff selection should reflect whether sensitivity or specificity is prioritized."""
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BibTeX
@misc{jin2026benchmarking,
title = {{Benchmarking miRNA Target Prediction Methods}},
author = {Congliang Jin and Weibo Niu and Zhexing Wen and Zhaohui S Qin and Ximing Ran},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.33960},
eprint = {2610.33960},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.33960},
}
RIS
TY - UNPB
TI - Benchmarking miRNA Target Prediction Methods
AU - Congliang Jin
AU - Weibo Niu
AU - Zhexing Wen
AU - Zhaohui S Qin
AU - Ximing Ran
PY - 2026
DA - 2026/10/25
PB - arXiv
DO - 10.48550/arxiv.2610.33960
UR - https://arxiv.org/abs/2610.33960
AB - Background: MicroRNAs (miRNAs) regulate gene expression by binding to target mRNAs. Although many tools predict miRNA-target interactions, predictions show limited concordance, and benchmarks relying on curated databases may be biased by overlap with tool-development data. Methods: We benchmarked seven tools (TargetScan, miRDB, miRmap, DIANA-microT, RNA22, miRWalk, and mirDIP) across approximately 59.7 million candidate miRNA-mRNA pairs. Each pair received an evidence-graded reference score (0-3) combining validated interactions from miRTarBase and TarBase with expression-derived evidence from paired miRNA-seq and mRNA-seq data in an induced pluripotent stem cell (iPSC) cohort of 22q11.2 deletion syndrome and control lines. Performance was evaluated using Type I error, statistical power, and false discovery proportion (FDP). Robustness was assessed by increasing reference-score stringency (truth signal) and including progressively lower-confidence predictions (model signal). Optimal cutoffs maximized Youden's J statistic. Results: Rankings were largely consistent across settings. TargetScan and miRDB maintained Type I error rates below 0.05, with TargetScan showing the lowest FDP, but both had limited power. mirDIP achieved the highest power at the cost of the highest Type I error rate. At optimal cutoffs, mirDIP best balanced sensitivity and specificity (Youden's J = 0.566), followed by DIANA-microT (J = 0.481); miRWalk and RNA22 had FDPs near the random-prediction baseline. No tool was optimal across all metrics; tool and cutoff selection should reflect whether sensitivity or specificity is prioritized."""
ER -