TY - GEN
T1 - Predicting Drug-Target Interactions by Node2vec Node Embedding in Molecular Associations Network
AU - Chen, Zhan Heng
AU - You, Zhu Hong
AU - Guo, Zhen Hao
AU - Yi, Hai Cheng
AU - Luo, Gong Xu
AU - Wang, Yan Bin
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Accurate identification of drug-target interactions (DTIs) is essential for drug development. It not only helps the researchers to understand the mechanism of drug action, but also contributes to the innovative drug discovery and repositioning. However, due to the limitation the high cost and long time, the traditional experimental methods are difficult to be widely applied for DTIs prediction. In this study, we propose an in silico method for predicting drug-target interactions by Node2vec node embedding in molecular associations network (MAN). Specifically, the MAN is constructed by integrating the associations among drug, protein, disease, lncRNA and miRNA. Then, the node2vec embedding method is employed to obtain a behavior feature vector of each node in the network. The traditional attribute feature vector comes from the drug molecular fingerprint and protein sequences. Finally, a random forest (RF) classifier is performed on these features to predict potential drug-target pairs. The experimental results show that the behavior feature could obtain 87.37% accuracy, which is obviously better than the traditional attribute feature. This work is not only more robust and reliable for predicting DTIs, but also provides an alternative way for other biomolecules associations prediction.
AB - Accurate identification of drug-target interactions (DTIs) is essential for drug development. It not only helps the researchers to understand the mechanism of drug action, but also contributes to the innovative drug discovery and repositioning. However, due to the limitation the high cost and long time, the traditional experimental methods are difficult to be widely applied for DTIs prediction. In this study, we propose an in silico method for predicting drug-target interactions by Node2vec node embedding in molecular associations network (MAN). Specifically, the MAN is constructed by integrating the associations among drug, protein, disease, lncRNA and miRNA. Then, the node2vec embedding method is employed to obtain a behavior feature vector of each node in the network. The traditional attribute feature vector comes from the drug molecular fingerprint and protein sequences. Finally, a random forest (RF) classifier is performed on these features to predict potential drug-target pairs. The experimental results show that the behavior feature could obtain 87.37% accuracy, which is obviously better than the traditional attribute feature. This work is not only more robust and reliable for predicting DTIs, but also provides an alternative way for other biomolecules associations prediction.
KW - Drug-target interactions
KW - Multi-molecular network
KW - Node2vec
UR - http://www.scopus.com/inward/record.url?scp=85094122612&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60802-6_31
DO - 10.1007/978-3-030-60802-6_31
M3 - 会议稿件
AN - SCOPUS:85094122612
SN - 9783030608019
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 348
EP - 358
BT - Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings
A2 - Huang, De-Shuang
A2 - Jo, Kang-Hyun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Intelligent Computing, ICIC 2020
Y2 - 2 October 2020 through 5 October 2020
ER -