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(A) deep neural network framework for drug-induced liver injury prediction using transcriptome data

(A) deep neural network framework for drug-induced liver injury prediction using transcriptome data

Material type
학위논문
Personal Author
황도영, 黃堵映
Title Statement
(A) deep neural network framework for drug-induced liver injury prediction using transcriptome data / Do Yeong Hwang
Publication, Distribution, etc
Seoul :   Graduate School, Korea University,   2020  
Physical Medium
iv, 23장 : 도표 ; 26 cm
기타형태 저록
A Deep Neural Network Framework for Drug-induced Liver Injury Prediction using Transcriptome Data   (DCOLL211009)000000127349  
학위논문주기
학위논문(석사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2020. 2
학과코드
0510   6D36   1107  
General Note
지도교수: 강재우  
Bibliography, Etc. Note
참고문헌: 장 20-23
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Graph Neural Network , Drug discovery,,
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090 ▼a 0510 ▼b 6D36 ▼c 1107
100 1 ▼a 황도영, ▼g 黃堵映
245 1 1 ▼a (A) deep neural network framework for drug-induced liver injury prediction using transcriptome data / ▼d Do Yeong Hwang
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2020
300 ▼a iv, 23장 : ▼b 도표 ; ▼c 26 cm
500 ▼a 지도교수: 강재우
502 0 ▼a 학위논문(석사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2020. 2
504 ▼a 참고문헌: 장 20-23
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Graph Neural Network ▼a Drug discovery
776 0 ▼t A Deep Neural Network Framework for Drug-induced Liver Injury Prediction using Transcriptome Data ▼w (DCOLL211009)000000127349
900 1 0 ▼a 강재우, ▼g 姜在雨, ▼e 지도교수
945 ▼a KLPA

Electronic Information

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(A) deep neural network framework for drug-induced liver injury prediction using transcriptome data (17회 열람)
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Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6D36 1107 Accession No. 123063729 Availability Available Due Date Make a Reservation Service B M
No. 2 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6D36 1107 Accession No. 123063730 Availability Available Due Date Make a Reservation Service B M

Contents information

Abstract

Drug-Induced Liver Injury (DILI) is a major cause of failed drug candidates in clinical trials and withdrawal of approved drugs from the market. Therefore, machine learning-based DILI prediction can be key in increasing the success rate of drug discovery because drug candidates that are predicted to potentially induce liver injury can be rejected before clinical trials. However, existing DILI prediction models mainly focus on the chemical structures of drugs. Since we cannot determine whether a drug will cause liver injury based solely on its structure, DILI prediction based on the transcriptional effect of a drug on a cell is necessary. 

In this paper, we propose GLIT which is a neural network trained on transcriptome data and chemical structures and can be used for drug-induced liver injury prediction. GLIT learns the embedding vectors of drug structures and drug-induced gene expression profiles using graph attention networks in a biological knowledge graph for predicting DILI. GLIT outperformed a baseline model that uses only drug structure information by 7\% and 19.2\% in terms of correct classification rate (CCR) and Matthews correlation coefficient (MCC), respectively. In addition, we conducted a literature survey to confirm whether the class labels of drugs, in the unknown DILI class, predicted by GLIT are correct. 

Table of Contents

1 Introduction 1
2 Methods 4
 2.1 Dataset description 4
 2.2 Problem statement 7
 2.3 Drug embedding layers  7
 2.4 Graph neural network layers for drug-induced gene expression profile representation 8
 2.5 Prediction layer  9
3 Results 10
 3.1 Dataset preparation  10
 3.2 Optimization 10
 3.3 Baseline models 11
 3.4 Evaluation results  12
 3.5 Qualitative analysis: drug embedding vector analysis 14
 3.6 Qualitative analysis: evaluation on the external dataset 15
4 Discussion 17
5 Conclusion 19
Bibliography 20

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