Link Prediction for Biomedical Network

Chau Pham, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationIAIT 2021 - 12th International Conference on Advances in Information Technology
Subtitle of host publicationIntelligence and Innovation for Digital Business and Society
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450390125
DOIs
StatePublished - Jun 29 2021
Event12th International Conference on Advances in Information Technology: Intelligence and Innovation for Digital Business and Society, IAIT 2021 - Virtual, Online, Thailand
Duration: Jun 29 2021Jul 1 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Advances in Information Technology: Intelligence and Innovation for Digital Business and Society, IAIT 2021
Country/TerritoryThailand
CityVirtual, Online
Period06/29/2107/1/21

Keywords

  • biomedical pathway
  • graph neural network
  • link prediction

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