TY - GEN
T1 - Inferring Disease-Associated Piwi-Interacting RNAs via Graph Attention Networks
AU - Zheng, Kai
AU - You, Zhu Hong
AU - Wang, Lei
AU - Wong, Leon
AU - Chen, Zhan Heng
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Piwi proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers. However, it is time-consuming and costly to detect piRNA-disease associations (PDAs) by traditional experimental methods. In this study, we present a computational method GAPDA to identify potential and biologically significant PDAs based on graph attention network. Specifically, we combined piRNA sequence information, disease semantic similarity, and piRNA-disease association network to construct a new attribute network. Then, the network embedding in node-level is learned via the attention-based graph neural network. Finally, potential piRNA-disease associations are scored.To be our knowledge, this is the first time that the attention-based Graph Neural Networks is introduced to the field of ncRNA-related association prediction. In the experiment, the proposed GAPDA method achieved AUC of 0.9038 using five-fold cross-validation. The experimental results show that the GAPDA approach ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.
AB - Piwi proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers. However, it is time-consuming and costly to detect piRNA-disease associations (PDAs) by traditional experimental methods. In this study, we present a computational method GAPDA to identify potential and biologically significant PDAs based on graph attention network. Specifically, we combined piRNA sequence information, disease semantic similarity, and piRNA-disease association network to construct a new attribute network. Then, the network embedding in node-level is learned via the attention-based graph neural network. Finally, potential piRNA-disease associations are scored.To be our knowledge, this is the first time that the attention-based Graph Neural Networks is introduced to the field of ncRNA-related association prediction. In the experiment, the proposed GAPDA method achieved AUC of 0.9038 using five-fold cross-validation. The experimental results show that the GAPDA approach ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.
KW - Disease
KW - Graph attention network
KW - piRNA-disease association
KW - PIWI-interacting RNA
KW - Self-attention mechanism
UR - http://www.scopus.com/inward/record.url?scp=85094109142&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60802-6_21
DO - 10.1007/978-3-030-60802-6_21
M3 - 会议稿件
AN - SCOPUS:85094109142
SN - 9783030608019
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 239
EP - 250
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 -
