Biological applications of knowledge graph embedding models

MOHAMED, Sameh K, Ayah NOUNU and Vít NOVÁČEK. Biological applications of knowledge graph embedding models. Briefings in Bioinformatics. Oxford (UK): Oxford University Press, 2021, vol. 22, No 2, p. 1679-1693. ISSN 1467-5463. Available from: https://dx.doi.org/10.1093/bib/bbaa012.

Basic information

• Original name: Biological applications of knowledge graph embedding models
• Name in Czech: Biological applications of knowledge graph embedding models
• Authors: MOHAMED, Sameh K, Ayah NOUNU and Vít NOVÁČEK.
• Edition: Briefings in Bioinformatics, Oxford (UK), Oxford University Press, 2021, 1467-5463.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 13.994
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1093/bib/bbaa012
• UT WoS: 000642298000084
• Keywords in English: biomedical knowledge graphs; knowledge graph embeddings; tensor factorization; link prediction; drug target interactions; polypharmacy side effects
• Tags: Artificial Intelligence, data integration, knowledge extraction, knowledge graphs, knowledge integration, relational machine learning
• Tags: International impact, Reviewed

Abstract

Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph’s inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug–target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.