GNN-based Biomedical Knowledge Graph Mining in Drug Development

Chang Su, Yu Hou, Fei Wang

Research output: Chapter in Book/Report/Conference proceedingChapter

4 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 languageEnglish (US)
Title of host publicationGraph Neural Networks
Subtitle of host publicationFoundations, Frontiers, and Applications
PublisherSpringer Nature
Pages517-540
Number of pages24
ISBN (Electronic)9789811660542
ISBN (Print)9789811660535
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

Bibliographical note

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

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