FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2950356072> ?p ?o ?g. }

• W2950356072 endingPage "3250" @default.
• W2950356072 startingPage "3240" @default.
• W2950356072 abstract "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." @default.
• W2950356072 created "2019-06-27" @default.
• W2950356072 creator A5001834977 @default.
• W2950356072 creator A5022557458 @default.
• W2950356072 creator A5042397246 @default.
• W2950356072 creator A5048276895 @default.
• W2950356072 creator A5049187207 @default.
• W2950356072 creator A5090872827 @default.
• W2950356072 creator A5091176232 @default.
• W2950356072 date "2019-06-12" @default.
• W2950356072 modified "2023-10-17" @default.
• W2950356072 title "Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance" @default.
• W2950356072 cites W1857672776 @default.
• W2950356072 cites W1964812476 @default.
• W2950356072 cites W1973562229 @default.
• W2950356072 cites W1983808777 @default.
• W2950356072 cites W1997696660 @default.
• W2950356072 cites W1999798000 @default.
• W2950356072 cites W2003626671 @default.
• W2950356072 cites W2009379608 @default.
• W2950356072 cites W2017783406 @default.
• W2950356072 cites W2021253486 @default.
• W2950356072 cites W2023250504 @default.
• W2950356072 cites W2028825255 @default.
• W2950356072 cites W2046589863 @default.
• W2950356072 cites W2049387089 @default.
• W2950356072 cites W2050844944 @default.
• W2950356072 cites W2065321039 @default.
• W2950356072 cites W2073976519 @default.
• W2950356072 cites W2076462394 @default.
• W2950356072 cites W2091819473 @default.
• W2950356072 cites W2108968943 @default.
• W2950356072 cites W2109501093 @default.
• W2950356072 cites W2117130368 @default.
• W2950356072 cites W2118086728 @default.
• W2950356072 cites W2148655675 @default.
• W2950356072 cites W2148708580 @default.
• W2950356072 cites W2153435542 @default.
• W2950356072 cites W2153635508 @default.
• W2950356072 cites W2154135985 @default.
• W2950356072 cites W2160697532 @default.
• W2950356072 cites W2180718817 @default.
• W2950356072 cites W2189695097 @default.
• W2950356072 cites W2234529989 @default.
• W2950356072 cites W2331386673 @default.
• W2950356072 cites W2397757171 @default.
• W2950356072 cites W2466044071 @default.
• W2950356072 cites W2519164774 @default.
• W2950356072 cites W2538201653 @default.
• W2950356072 cites W2553997020 @default.
• W2950356072 cites W2581082771 @default.
• W2950356072 cites W2587017960 @default.
• W2950356072 cites W2588003167 @default.
• W2950356072 cites W2599171418 @default.
• W2950356072 cites W2609161598 @default.
• W2950356072 cites W2610646689 @default.
• W2950356072 cites W2618135456 @default.
• W2950356072 cites W2676147597 @default.
• W2950356072 cites W2731260319 @default.
• W2950356072 cites W2736074174 @default.
• W2950356072 cites W2754293187 @default.
• W2950356072 cites W2767582147 @default.
• W2950356072 cites W2770796707 @default.
• W2950356072 cites W2771073546 @default.
• W2950356072 cites W2775061087 @default.
• W2950356072 cites W2783606427 @default.
• W2950356072 cites W2887381903 @default.
• W2950356072 cites W3100704554 @default.
• W2950356072 cites W3106417893 @default.
• W2950356072 cites W4229880569 @default.
• W2950356072 cites W4253759977 @default.
• W2950356072 doi "https://doi.org/10.1021/acs.jcim.9b00143" @default.
• W2950356072 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31188585" @default.
• W2950356072 hasPublicationYear "2019" @default.
• W2950356072 type Work @default.
• W2950356072 sameAs 2950356072 @default.
• W2950356072 citedByCount "20" @default.
• W2950356072 countsByYear W29503560722020 @default.
• W2950356072 countsByYear W29503560722021 @default.
• W2950356072 countsByYear W29503560722022 @default.
• W2950356072 countsByYear W29503560722023 @default.
• W2950356072 crossrefType "journal-article" @default.
• W2950356072 hasAuthorship W2950356072A5001834977 @default.
• W2950356072 hasAuthorship W2950356072A5022557458 @default.
• W2950356072 hasAuthorship W2950356072A5042397246 @default.
• W2950356072 hasAuthorship W2950356072A5048276895 @default.
• W2950356072 hasAuthorship W2950356072A5049187207 @default.
• W2950356072 hasAuthorship W2950356072A5090872827 @default.
• W2950356072 hasAuthorship W2950356072A5091176232 @default.
• W2950356072 hasConcept C108583219 @default.
• W2950356072 hasConcept C117220453 @default.
• W2950356072 hasConcept C119857082 @default.
• W2950356072 hasConcept C12267149 @default.
• W2950356072 hasConcept C124101348 @default.
• W2950356072 hasConcept C127413603 @default.
• W2950356072 hasConcept C132964779 @default.
• W2950356072 hasConcept C154945302 @default.
• W2950356072 hasConcept C164085508 @default.

• next