J 2021

Biological applications of knowledge graph embedding models

MOHAMED, Sameh K, Ayah NOUNU and Vít NOVÁČEK

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

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 13.994

Organization unit

Faculty of Informatics

DOI

UT WoS

000642298000084

Keywords in English

biomedical knowledge graphs; knowledge graph embeddings; tensor factorization; link prediction; drug target interactions; polypharmacy side effects

Tags

Tags

International impact, Reviewed
Změněno: 7/4/2024 23:33, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

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.