Bioinformatics and Medical Applications

5Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health

Akanksha Jaiswar^1†, Devender Arora^2†, Manisha Malhotra³, Abhimati Shukla⁴ and Nivedita Rai⁵*

¹Centre for Agricultural Bioinformatics (CABin), ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India

²National Institute of Animal Science, Rural Development Administration, Jeonju, South Korea

³CSIR-Indian Institute of Petroleum, Dehradun, India

⁴Harcourt Butler Technical University, Kanpur, India

⁵School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India

Abstract

In this data-driven world, recent technologies are producing high-throughput genomics and biomedical data extensively. An enormous amount of relational data evolves that can interlink the genes, proteins, chemical compounds, drugs, and diseases. As biological networks are very complex and hard to interpret, a significant amount of progress is going on toward the graph or network embedding paradigm that can use for visualization, representation, interpretation, and their correlation. In this technique, diverse input data are systematically integrated to generate a unified vector representation. The current chapter mainly focuses on traditional and current development on the network or graph embedding and their applications in computational biology, genomics, and healthcare. Finally, to gain more biological insight, further quantification and evaluation of the network ...