南京大学学报（自然科学版）2026，Vol.62Issue(4)：647-656,10.DOI:10.13232/j.cnki.jnju.2026.04.010
siRNA效率预测的不确定性评估及数据筛选策略
Uncertainty quantification and data screening strategies for siRNA efficacy prediction
摘要
Abstract
RNA interference(RNAi)is a promising therapeutic strategy.However,existing siRNA efficacy predictors lack uncertainty quantification.Here,we present an OligoFormer-based model to quantify both model and data uncertainty using the Huesken and Mixset datasets.Our analysis reveals that data uncertainty dominates overall uncertainty.Using it as a filter,we derived low-uncertainty subsets,denoted as Huesken'and Mixset'.In cross-validation,the Huesken'achieved AUC,PRC,F1-score,and PCC scores of 0.950,0.941,0.936,and 0.791,respectively,while Mixset'scored 0.901,0.930,0.773,and 0.733,surpassing the performance on the original data.In cross-dataset tests,models trained on Huesken'outperformed those on the original data,with AUC,PRC,and PCC improving by 1.29%,0.93%,and 0.72%,respectively.Uncertainty estimation thus enhances both prediction confidence and model generalizability via data filtering.Crucially,for deep learning-based siRNA prediction,improving data quality is more impactful than increasing data quantity.关键词
siRNA效率预测/不确定性量化/模型不确定度/数据不确定度/数据筛选Key words
siRNA efficacy prediction/uncertainty quantification/model uncertainty/data uncertainty/data filtering分类
信息技术与安全科学引用本文复制引用
张睿格,杨育行,孙硕,张建,王炜..siRNA效率预测的不确定性评估及数据筛选策略[J].南京大学学报（自然科学版）,2026,62(4):647-656,10.基金项目
科技部科技创新项目(2030-2021ZD0201300) （2030-2021ZD0201300）
