To embed or not: Network embedding as a paradigm in computational biology

Walter Nelson, Marinka Zitnik, Bo Wang, Jure Leskovec, Anna Goldenberg, Roded Sharan

Research output: Contribution to journalReview articlepeer-review


Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.

Original languageEnglish
Article number381
JournalFrontiers in Genetics
Issue numberMAY
StatePublished - 2019


  • Community detection
  • Network alignment
  • Network biology
  • Network embedding
  • Protein function prediction

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)


Dive into the research topics of 'To embed or not: Network embedding as a paradigm in computational biology'. Together they form a unique fingerprint.

Cite this