Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance

Drug-induced liver injury (DILI),
one of the most common adverse
effects, leads to drug development failure or withdrawal from the
market in most cases, showing an emerging challenge that is to accurately
predict DILI in the early stage. Recently, the vast amount of gene
expression data provides us valuable information for distinguishing
DILI on a genomic scale. Moreover, the deep learning algorithm is
a powerful strategy to automatically learn important features from
raw and noisy data and shows great success in the field of medical
diagnosis. In this study, a gene expression data based deep learning
model was developed to predict DILI in advance by using gene expression
data associated with DILI collected from ArrayExpress and then optimized
by feature gene selection and parameters optimization. In addition,
the previous machine learning algorithm support vector machine (SVM)
was also used to construct another prediction model based on the same
data sets, comparing the model performance with the optimal DL model.
Finally, the evaluation test using 198 randomly selected samples showed
that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity,
96.8% specificity, 0.942 matthews correlation coefficient, and 0.989
area under the ROC curve, while the performance of SVM model only
reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794
matthews correlation coefficient, and 0.901 area under the ROC curve.
Furthermore, external data sets verification and animal experiments
were conducted to assess the optimal DL model performance. Finally,
the predicted results of the optimal DL model were almost consistent
with experiment results. These results indicated that our gene expression
data based deep learning model could systematically and accurately
predict DILI in advance. It could be a useful tool to provide safety
information for drug discovery and clinical rational drug use in early
stage and become an important part of drug safety assessment.