GNN-based Biomedical Knowledge Graph Mining in Drug Development

Chang Su; Yu Hou; Fei Wang

doi:10.1007/978-981-16-6054-2_24

GNN-based Biomedical Knowledge Graph Mining in Drug Development

Chang Su
, Yu Hou
, Fei Wang

Research output: Chapter in Book/Report/Conference proceeding › Chapter

11 Scopus citations

Abstract

In this chapter, we first describe what representation learning is and why we need representation learning. Among the various ways of learning representations, this chapter focuses on deep learning methods: those that are formed by the composition of multiple non-linear transformations, with the goal of resulting in more abstract and ultimately more useful representations. We summarize the representation learning techniques in different domains, focusing on the unique challenges and models for different data types including images, natural languages, speech signals and networks. Last, we summarize this chapter.

Original language	English (US)
Title of host publication	Graph Neural Networks
Subtitle of host publication	Foundations, Frontiers, and Applications
Publisher	Springer Nature
Pages	517-540
Number of pages	24
ISBN (Electronic)	9789811660542
ISBN (Print)	9789811660535
DOIs	https://doi.org/10.1007/978-981-16-6054-2_24
State	Published - Jan 1 2022
Externally published	Yes

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© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

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10.1007/978-981-16-6054-2_24

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GNN-based Biomedical Knowledge Graph Mining in Drug Development

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