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Gigascience. 2018 Apr 1;7(4):1-31. doi: 10.1093/gigascience/giy014.

Bipartite graphs in systems biology and medicine: a survey of methods and applications.

Author information

1
Lawrence Berkeley Labs, DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA.
2
University of Thessaly, Department of Computer Science and Biomedical Informatics, Papasiopoulou 2-4, Lamia, 35100, Greece.
3
Izmir International Biomedicine and Genome Institute (iBG-Izmir), Dokuz Eylül University, 35340, Turkey.
4
Université Paris Diderot, Sorbonne Paris Cité, Epigenetics and Cell Fate, UMR7216, CNRS, France.

Abstract

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

PMID:
29648623
PMCID:
PMC6333914
DOI:
10.1093/gigascience/giy014
[Indexed for MEDLINE]
Free PMC Article

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