Ji, XiangminWang, LeiHua, LiyanWang, XueyingZhang, PengyueShendre, AditiFeng, WeixingLi, JinLi, Lang2024-10-212024-10-212021Ji X, Wang L, Hua L, et al. Improved Adverse Drug Event Prediction Through Information Component Guided Pharmacological Network Model (IC-PNM). IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2021;18(3):1113-1121. doi:10.1109/TCBB.2019.2928305https://hdl.handle.net/1805/44093Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve (\textAUROC)= 0.82(AUROC)=0.82. On the other hand, the IC-PNM prediction performance improved to \textAUROC= 0.91AUROC=0.91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.en-USAttribution 4.0 InternationalAdverse drug eventBiological system modelingData modelsDatabasesDrugsInformation componentIntegrated circuitsPharmacological network modelPharmacovigilancePredictive modelsTraining dataArticle