To embed or not: network embedding as a paradigm in computational biology
- 1Sick Kids Research Institute, Canada
- 2Stanford University, United States
- 3University of Toronto, Canada
- 4Vector Institute, Canada
- 5Tel Aviv University, Israel
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.
Keywords: network biology, Network embedding, Network alignment, community detection, protein function prediction
Received: 05 Feb 2019;
Accepted: 09 Apr 2019.
Edited by:Marco Pellegrini, Italian National Research Council (CNR), Italy
Reviewed by:Gregorio Alanis-Lobato, Francis Crick Institute, United Kingdom
Noel Malod-Dognin, Barcelona Supercomputing Center, Spain
Copyright: © 2019 Nelson, Zitnik, Wang, Leskovec, Goldenberg and Sharan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Prof. Anna Goldenberg, University of Toronto, Toronto, M5S 1A1, Ontario, Canada, firstname.lastname@example.org
Prof. Roded Sharan, Tel Aviv University, Tel Aviv, Israel, email@example.com