id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
10_1101-2021_02_10_430512	Kim, Catherine	Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification	2021	41	.pdf	application/pdf	11859	1137	56	into DDIs. In this study, a hierarchical machine learning model was created to predict DDIassociated ADRs and pharmacological insight thereof for any drug pair. drugs' chemical structures as inputs to predict their target, enzyme, and transporter (TET) Development of RFCs for Prediction of Target, Enzyme, and Transporter Profiles of Drugs Development of a Model for Prediction of DDI-associated ADRs from TET Profiles of Drugs ADR prediction from Target, Enzyme, and Transporter Profiles of Drug Pairs To predict ADRs of a drug pair from its TET profiles, Random Forest Classifier (RFC), Application of the SVM model for DDI-associated ADRs Involving Three Major Drugs through predicted PRR changes of drug pairs upon removal of each of the targets, enzymes, and changes of drug pairs were predicted by the model upon removal of each of the targets, enzymes, Target, enzyme, and transporter (TET) profiles of atorvastatin and concomitant drugs, 	./cache/10_1101-2021_02_10_430512.pdf	./txt/10_1101-2021_02_10_430512.txt