Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

• Published in: Briefings in bioinformatics Vol. 19; no. 6; pp. 1370 - 1381
• Main Authors: Yan, Jingwen, Risacher, Shannon L, Shen, Li, Saykin, Andrew J
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 27-11-2018; Oxford Publishing Limited (England)
• Subjects: Biology; Data collection; Data Mining; Epistasis, Genetic; Gene expression; Genomes; Genomics; Machine Learning; Proteomes; Proteomics; Systems Biology; Transcriptome; systems biology; computational methods; network approaches; integrative omics
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbx066

Abstract
In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.