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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

자료유형
학위논문
개인저자
황도영, 黃堵映
서명 / 저자사항
(A) deep neural network framework for drug-induced liver injury prediction using transcriptome data / Do Yeong Hwang
발행사항
Seoul :   Graduate School, Korea University,   2020  
형태사항
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  
일반주기
지도교수: 강재우  
서지주기
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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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

전자정보

No. 원문명 서비스
1
(A) deep neural network framework for drug-induced liver injury prediction using transcriptome data (17회 열람)
PDF 초록 목차

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1107 등록번호 123063729 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1107 등록번호 123063730 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

초록

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. 

목차

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