TY - GEN
T1 - Predicting Drug-Disease Associations Based on Network Consistency Projection
AU - Zhang, Qiang
AU - Zuo, Zonglan
AU - Yan, Rui
AU - Zheng, Chunhou
AU - Zhang, Fa
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - With the increasing cost of traditional drug discovery, drug repositioning methods at low cost have attracting increasing attention. The generation of large amounts of biomedical data also provides unprecedented opportunities for drug repositioning research. However, how to effectively integrate different types of data is still a challenge for drug repositioning. In this paper, we propose a computational method using Network Consistency Projection for Drug-Disease Association (NCPDDA) prediction. First of all, our method proposes a new method for calculating one type of disease similarity. Moreover, since effective integration of data from multiple sources can improve prediction performance, the NCPDDA integrates multiple kinds of similarities. Then, considering that noise may affect the prediction performance of the model, the NCPDDA uses the similarity network fusion method to reduce the impact of noise. Finally, the network consistency projection is used to predict potential drug-disease associations. NCPDDA is compared with several classical drug repositioning methods, and the experimental results show that NCPDDA is superior to these methods. Moreover, the study of several representative drugs proves the practicality of NCPDDA in practical application.
AB - With the increasing cost of traditional drug discovery, drug repositioning methods at low cost have attracting increasing attention. The generation of large amounts of biomedical data also provides unprecedented opportunities for drug repositioning research. However, how to effectively integrate different types of data is still a challenge for drug repositioning. In this paper, we propose a computational method using Network Consistency Projection for Drug-Disease Association (NCPDDA) prediction. First of all, our method proposes a new method for calculating one type of disease similarity. Moreover, since effective integration of data from multiple sources can improve prediction performance, the NCPDDA integrates multiple kinds of similarities. Then, considering that noise may affect the prediction performance of the model, the NCPDDA uses the similarity network fusion method to reduce the impact of noise. Finally, the network consistency projection is used to predict potential drug-disease associations. NCPDDA is compared with several classical drug repositioning methods, and the experimental results show that NCPDDA is superior to these methods. Moreover, the study of several representative drugs proves the practicality of NCPDDA in practical application.
KW - Drug repositioning
KW - Drug-disease association
KW - Network consistency projection
KW - Similarity network fusion
UR - http://www.scopus.com/inward/record.url?scp=85113812256&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-84532-2_53
DO - 10.1007/978-3-030-84532-2_53
M3 - Conference contribution
AN - SCOPUS:85113812256
SN - 9783030845315
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 591
EP - 602
BT - Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings
A2 - Huang, De-Shuang
A2 - Jo, Kang-Hyun
A2 - Li, Jianqiang
A2 - Gribova, Valeriya
A2 - Bevilacqua, Vitoantonio
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Intelligent Computing, ICIC 2021
Y2 - 12 August 2021 through 15 August 2021
ER -