TY - GEN
T1 - Predicting Drug-Disease Associations via Meta-path Representation Learning based on Heterogeneous Information Net works
AU - Zhang, Meng Long
AU - Zhao, Bo Wei
AU - Hu, Lun
AU - You, Zhu Hong
AU - Chen, Zhan Heng
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Identifying new indications of drugs plays an important role in the drug research and development process. However, traditional methods are labor-intensive and financially demanding to discover new indications. Computational methods are regarded as an effective way to predict underlying drug-disease associations (DDAs). Therefore it is a great urgent to develop computational-based methods to improve the accuracy of DDAs prediction. In this paper, a novel Meta-path Representation Learning-based model called MRLDDA is proposed to predict new DDAs on a heterogeneous information network (HIN). Specifically, MRLDDA first constructs a meta-path strategy based on rich HIN, i.e., drug-protein-disease-drug, and then the network representation of drugs and diseases is obtained by a heterogeneous representation model. Finally, a typical machine learning strategy--random forest classifier is applied to solve the prediction task of DDAs. Experimental results on the two benchmark datasets show that MRLDDA has a better prediction performance for the new DDAs under ten-fold cross-validation, with AUC of 0.8427 on B-Dataset and 0.9482 on F-Dataset.
AB - Identifying new indications of drugs plays an important role in the drug research and development process. However, traditional methods are labor-intensive and financially demanding to discover new indications. Computational methods are regarded as an effective way to predict underlying drug-disease associations (DDAs). Therefore it is a great urgent to develop computational-based methods to improve the accuracy of DDAs prediction. In this paper, a novel Meta-path Representation Learning-based model called MRLDDA is proposed to predict new DDAs on a heterogeneous information network (HIN). Specifically, MRLDDA first constructs a meta-path strategy based on rich HIN, i.e., drug-protein-disease-drug, and then the network representation of drugs and diseases is obtained by a heterogeneous representation model. Finally, a typical machine learning strategy--random forest classifier is applied to solve the prediction task of DDAs. Experimental results on the two benchmark datasets show that MRLDDA has a better prediction performance for the new DDAs under ten-fold cross-validation, with AUC of 0.8427 on B-Dataset and 0.9482 on F-Dataset.
KW - Diseases
KW - Drug-disease associations
KW - Drugs
KW - Heterogeneous information network
KW - Meta-path generation strategy
UR - http://www.scopus.com/inward/record.url?scp=85139246542&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-13829-4_18
DO - 10.1007/978-3-031-13829-4_18
M3 - 会议稿件
AN - SCOPUS:85139246542
SN - 9783031138287
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 220
EP - 232
BT - Intelligent Computing Theories and Application - 18th International Conference, ICIC 2022, Proceedings
A2 - Huang, De-Shuang
A2 - Jo, Kang-Hyun
A2 - Jing, Junfeng
A2 - Premaratne, Prashan
A2 - Bevilacqua, Vitoantonio
A2 - Hussain, Abir
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Intelligent Computing, ICIC 2022
Y2 - 7 August 2022 through 11 August 2022
ER -