TY - GEN
T1 - Link Prediction for Biomedical Network
AU - Pham, Chau
AU - Dang, Tommy
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/6/29
Y1 - 2021/6/29
N2 - Network datasets are seen ubiquity in many fields, such as protein interactions, paper citation, and social networks. While some networks are well-defined, many others are not. For example, the interactions of proteins in cancer pathways are still studied by system biologists and medical researchers. Therefore, one of the primary analytic tasks to perform on these networks is link prediction, where we desire to reveal some unknown relationships with certain levels of confidence. In this paper, we carry out some experiments on network datasets in the biomedical domain using state-of-the-art Graph Neural Networks. The results show that entity's values facilitate graph-based models to perform well on uncovering latent relationships in biomedical research and potentially be extended on other application domains.
AB - Network datasets are seen ubiquity in many fields, such as protein interactions, paper citation, and social networks. While some networks are well-defined, many others are not. For example, the interactions of proteins in cancer pathways are still studied by system biologists and medical researchers. Therefore, one of the primary analytic tasks to perform on these networks is link prediction, where we desire to reveal some unknown relationships with certain levels of confidence. In this paper, we carry out some experiments on network datasets in the biomedical domain using state-of-the-art Graph Neural Networks. The results show that entity's values facilitate graph-based models to perform well on uncovering latent relationships in biomedical research and potentially be extended on other application domains.
KW - biomedical pathway
KW - graph neural network
KW - link prediction
UR - http://www.scopus.com/inward/record.url?scp=85112243146&partnerID=8YFLogxK
U2 - 10.1145/3468784.3471608
DO - 10.1145/3468784.3471608
M3 - Conference contribution
AN - SCOPUS:85112243146
T3 - ACM International Conference Proceeding Series
BT - IAIT 2021 - 12th International Conference on Advances in Information Technology
PB - Association for Computing Machinery
T2 - 12th International Conference on Advances in Information Technology: Intelligence and Innovation for Digital Business and Society, IAIT 2021
Y2 - 29 June 2021 through 1 July 2021
ER -