Journal Article

Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

Liu, Zhichao, Li, Ting, Tong, Weida, Thakkar, Shraddha, Roberts, Ruth

2020

Overview

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K -nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Algorithms / Annotations / Bioengineering and Biotechnology / Cell lines / Classification / Datasets / Deep learning / deep learning–artificial neural network / DILI / Drug dosages / FDA approval / Genomics / high throughput transcriptomics / Investigations / Liver / machine learning / Neural networks / Predictions / risk assessment / Support vector machines / Toxicity / toxicity prediction model / Transcriptomics