4.7 Article

Biological network analysis with deep learning

期刊

BRIEFINGS IN BIOINFORMATICS
卷 22, 期 2, 页码 1515-1530

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa257

关键词

deep learning; biological networks; protein function prediction; protein interaction prediction; drug development; drug-target prediction

资金

  1. Alfried Krupp Prize for Young University Teachers of the Alfried Krupp von Bohlen und Halbach-Stiftung
  2. European Union [813533]
  3. Marie Curie Actions (MSCA) [813533] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology, creating a need for new computational tools to analyze networks. Graph neural networks (GNNs) are being frequently applied in bioinformatics for tasks such as protein function prediction, protein-protein interaction prediction, and in silico drug discovery and development. Deep learning is emerging as a new tool to answer classic questions in areas like gene regulatory networks and disease diagnosis.
Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

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