Biological applications of knowledge graph embedding models

MOHAMED, Sameh K, Ayah NOUNU a Vít NOVÁČEK. Biological applications of knowledge graph embedding models. Briefings in Bioinformatics. Oxford (UK): Oxford University Press, 2021, roč. 22, č. 2, s. 1679-1693. ISSN 1467-5463. Dostupné z: https://dx.doi.org/10.1093/bib/bbaa012.

Základní údaje

• Originální název: Biological applications of knowledge graph embedding models
• Název česky: Biological applications of knowledge graph embedding models
• Autoři: MOHAMED, Sameh K, Ayah NOUNU a Vít NOVÁČEK.
• Vydání: Briefings in Bioinformatics, Oxford (UK), Oxford University Press, 2021, 1467-5463.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Velká Británie a Severní Irsko
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Impakt faktor: Impact factor: 13.994
• Organizační jednotka: Fakulta informatiky
• Doi: http://dx.doi.org/10.1093/bib/bbaa012
• UT WoS: 000642298000084
• Klíčová slova anglicky: biomedical knowledge graphs; knowledge graph embeddings; tensor factorization; link prediction; drug target interactions; polypharmacy side effects
• Štítky: Artificial Intelligence, data integration, knowledge extraction, knowledge graphs, knowledge integration, relational machine learning
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.