A drug-induced liver injury prediction model using transcriptional response data with graph neural network

Doyeong Hwang, Minji Jeon, Jaewoo Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 model that uses transcriptional response 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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
EditorsWookey Lee, Luonan Chen, Yang-Sae Moon, Julien Bourgeois, Mehdi Bennis, Yu-Feng Li, Young-Guk Ha, Hyuk-Yoon Kwon, Alfredo Cuzzocrea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages323-329
Number of pages7
ISBN (Electronic)9781728160344
DOIs
Publication statusPublished - 2020 Feb
Event2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 - Busan, Korea, Republic of
Duration: 2020 Feb 192020 Feb 22

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020

Conference

Conference2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
CountryKorea, Republic of
CityBusan
Period20/2/1920/2/22

Keywords

  • Drug Discovery
  • Drug induced Liver Injury
  • Drug-induced Gene Expression Profiles
  • Graph Neural Network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems and Management
  • Control and Optimization

Cite this

Hwang, D., Jeon, M., & Kang, J. (2020). A drug-induced liver injury prediction model using transcriptional response data with graph neural network. In W. Lee, L. Chen, Y-S. Moon, J. Bourgeois, M. Bennis, Y-F. Li, Y-G. Ha, H-Y. Kwon, & A. Cuzzocrea (Eds.), Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 (pp. 323-329). [9070697] (Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp48618.2020.00-54