PMID- 33330410
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201218
IS  - 2296-4185 (Print)
IS  - 2296-4185 (Electronic)
IS  - 2296-4185 (Linking)
VI  - 8
DP  - 2020
TI  - Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver 
      Injury.
PG  - 562677
LID - 10.3389/fbioe.2020.562677 [doi]
LID - 562677
AB  - 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.
CI  - Copyright (c) 2020 Li, Tong, Roberts, Liu and Thakkar.
FAU - Li, Ting
AU  - Li T
AD  - Division of Bioinformatics and Biostatistics, National Center for Toxicological 
      Research, U.S. Food and Drug Administration, Jefferson, AR, United States.
AD  - Joint Bioinformatics Program, University of Arkansas at Little Rock and 
      University of Arkansas for Medical Sciences, Little Rock, AR, United States.
FAU - Tong, Weida
AU  - Tong W
AD  - Division of Bioinformatics and Biostatistics, National Center for Toxicological 
      Research, U.S. Food and Drug Administration, Jefferson, AR, United States.
FAU - Roberts, Ruth
AU  - Roberts R
AD  - Division of Bioinformatics and Biostatistics, National Center for Toxicological 
      Research, U.S. Food and Drug Administration, Jefferson, AR, United States.
AD  - ApconiX Ltd., Alderley Edge, United Kingdom.
AD  - Department of Biosciences, University of Birmingham, Birmingham, United Kingdom.
FAU - Liu, Zhichao
AU  - Liu Z
AD  - Division of Bioinformatics and Biostatistics, National Center for Toxicological 
      Research, U.S. Food and Drug Administration, Jefferson, AR, United States.
FAU - Thakkar, Shraddha
AU  - Thakkar S
AD  - Division of Bioinformatics and Biostatistics, National Center for Toxicological 
      Research, U.S. Food and Drug Administration, Jefferson, AR, United States.
LA  - eng
PT  - Journal Article
DEP - 20201127
PL  - Switzerland
TA  - Front Bioeng Biotechnol
JT  - Frontiers in bioengineering and biotechnology
JID - 101632513
PMC - PMC7728858
OTO - NOTNLM
OT  - DILI
OT  - deep learning-artificial neural network
OT  - high throughput transcriptomics
OT  - machine learning
OT  - risk assessment
OT  - toxicity prediction model
COIS- RR is co-founder and co-director of ApconiX, an integrated toxicology and ion 
      channel company that provides expert advice on non-clinical aspects of drug 
      discovery and drug development to academia, industry, and not-for-profit 
      organizations. The remaining authors declare that the research was conducted in 
      the absence of any commercial or financial relationships that could be construed 
      as a potential conflict of interest.
EDAT- 2020/12/18 06:00
MHDA- 2020/12/18 06:01
PMCR- 2020/01/01
CRDT- 2020/12/17 05:53
PHST- 2020/05/15 00:00 [received]
PHST- 2020/11/05 00:00 [accepted]
PHST- 2020/12/17 05:53 [entrez]
PHST- 2020/12/18 06:00 [pubmed]
PHST- 2020/12/18 06:01 [medline]
PHST- 2020/01/01 00:00 [pmc-release]
AID - 10.3389/fbioe.2020.562677 [doi]
PST - epublish
SO  - Front Bioeng Biotechnol. 2020 Nov 27;8:562677. doi: 10.3389/fbioe.2020.562677. 
      eCollection 2020.