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Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728858/ https://www.ncbi.nlm.nih.gov/pubmed/33330410 http://dx.doi.org/10.3389/fbioe.2020.562677 |
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author | Li, Ting Tong, Weida Roberts, Ruth Liu, Zhichao Thakkar, Shraddha |
author_facet | Li, Ting Tong, Weida Roberts, Ruth Liu, Zhichao Thakkar, Shraddha |
author_sort | Li, Ting |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7728858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77288582020-12-15 Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury Li, Ting Tong, Weida Roberts, Ruth Liu, Zhichao Thakkar, Shraddha Front Bioeng Biotechnol Bioengineering and Biotechnology 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. Frontiers Media S.A. 2020-11-27 /pmc/articles/PMC7728858/ /pubmed/33330410 http://dx.doi.org/10.3389/fbioe.2020.562677 Text en Copyright © 2020 Li, Tong, Roberts, Liu and Thakkar. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Li, Ting Tong, Weida Roberts, Ruth Liu, Zhichao Thakkar, Shraddha Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury |
title | Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury |
title_full | Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury |
title_fullStr | Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury |
title_full_unstemmed | Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury |
title_short | Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury |
title_sort | deep learning on high-throughput transcriptomics to predict drug-induced liver injury |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728858/ https://www.ncbi.nlm.nih.gov/pubmed/33330410 http://dx.doi.org/10.3389/fbioe.2020.562677 |
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