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

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...

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Bibliographic Details
Main Author: Chunlai Feng (2228515) (author)
Other Authors: Hengwei Chen (6892724) (author), Xianqin Yuan (6892727) (author), Mengqiu Sun (6892730) (author), Kexin Chu (6892733) (author), Hanqin Liu (2969421) (author), Mengjie Rui (3589193) (author)
Published: 2019
Subjects:
Genetics
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
SVM
gene expression data
data sets verification
drug safety assessment
Advance Drug-induced liver injury
DL model
Drug-Induced Liver Injury
0.794 matthews correlation coefficient
DL model performance
DILI
algorithm support vector machine
feature gene selection
gene Expression Data
drug development failure
ROC curve
0.942 matthews correlation coefficient
Deep Learning Model
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