siRNA效率预测的不确定性评估及数据筛选策略OA

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.