Learning Structural Genetic Information via Graph Neural Embedding

Yuan Xie, Yulong Pei, Yun Lu, Haixu Tang, Yuan Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Learning continuous vector representations of genes has been proved to be conducive for many bioinformatics tasks as it can incorporate information of various sources including gene interactions and gene-disease interactions. However, most of the existing approaches, following a paradigm stemmed from the natural language processing community, treat the embedding context in a flat fashion such as a sequence, and tend to overlook the fact that proteins are more likely to function together. In this study, we propose an unsupervised gene embedding algorithm which utilizes graph convolutional network to learn structural information of genes from their neighborhoods in genetic interaction networks. We also propose a neighborhood sampling strategy to generate training samples. Our approach does not assume conditional independence of the node neighborhood and focuses on learning structural information. We compare our method against state-of-the-art baselines and experimental results demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - 16th International Symposium, ISBRA 2020, Proceedings
EditorsZhipeng Cai, Ion Mandoiu, Giri Narasimhan, Pavel Skums, Xuan Guo
Number of pages12
ISBN (Print)9783030578206
StatePublished - 2020
Event16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020 - Moscow, Russian Federation
Duration: Dec 1 2020Dec 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12304 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020
Country/TerritoryRussian Federation


  • Essential gene identification
  • Gene embedding
  • Graph convolutional network
  • Protein-protein interaction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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