Graph representation learning in bioinformatics: trends, methods and applications

Abstract Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with d...

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Vydáno v:Briefings in bioinformatics Ročník 23; číslo 1
Hlavní autoři: Yi, Hai-Cheng, You, Zhu-Hong, Huang, De-Shuang, Kwoh, Chee Keong
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 17.01.2022
Oxford Publishing Limited (England)
Témata:
Algorithms
Bioinformatics
Complex systems
Computational Biology - methods
Data structures
Deep learning
Embedding
Graph neural networks
Graph representations
Graphical representations
Graphs
Knowledge
Knowledge representation
Learning algorithms
Machine Learning
Neural networks
Neural Networks, Computer
Topology
deep learning
graph embedding
graph neural network
graph representation learning
knowledge graph
healthcare
ISSN:1467-5463, 1477-4054, 1477-4054
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Shrnutí:Abstract Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain. However, rich relational information between biological elements is retained in the non-Euclidean biomedical graphs, which is not learning friendly to classic machine learning methods. Graph representation learning aims to embed graph into a low-dimensional space while preserving graph topology and node properties. It bridges biomedical graphs and modern machine learning methods and has recently raised widespread interest in both machine learning and bioinformatics communities. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. To provide a comprehensive and structured analysis and perspective, we first categorize and analyze both graph embedding methods (homogeneous graph embedding, heterogeneous graph embedding, attribute graph embedding) and graph neural networks. Furthermore, we summarize their representative applications from molecular level to genomics, pharmaceutical and healthcare systems level. Moreover, we provide open resource platforms and libraries for implementing these graph representation learning methods and discuss the challenges and opportunities of graph representation learning in bioinformatics. This work provides a comprehensive survey of emerging graph representation learning algorithms and their applications in bioinformatics. It is anticipated that it could bring valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbab340