TY - GEN
T1 - Predicting Missing and Spurious Protein-Protein Interactions Using Graph Embeddings on GO Annotation Graph
AU - Zhong, Xiaoshi
AU - Rajapakse, Jagath C.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Protein-protein interaction (PPI) prediction is a key step towards many bioinformatics applications including prediction of protein functions and drug-disease interactions. However, previous research on PPI prediction rarely considered missing and spurious interactions in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a novel method that employs graph embeddings to learn vector representations from constructed Gene Ontology (GO) annotation graphs. Our method leverages the information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. We compare our method with methods based on information content and on word embeddings, using three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.
AB - Protein-protein interaction (PPI) prediction is a key step towards many bioinformatics applications including prediction of protein functions and drug-disease interactions. However, previous research on PPI prediction rarely considered missing and spurious interactions in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a novel method that employs graph embeddings to learn vector representations from constructed Gene Ontology (GO) annotation graphs. Our method leverages the information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. We compare our method with methods based on information content and on word embeddings, using three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.
KW - GO annotation graph
KW - graph embeddings
KW - missing protein interactions
KW - protein-protein interactions
KW - spurious protein interactions
UR - http://www.scopus.com/inward/record.url?scp=85084333611&partnerID=8YFLogxK
U2 - 10.1109/BIBM47256.2019.8983323
DO - 10.1109/BIBM47256.2019.8983323
M3 - Conference contribution
AN - SCOPUS:85084333611
T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
SP - 1828
EP - 1835
BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
A2 - Yoo, Illhoi
A2 - Bi, Jinbo
A2 - Hu, Xiaohua Tony
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Y2 - 18 November 2019 through 21 November 2019
ER -
