Separating the drivers from the driven: Integrative network and pathway approaches aid identification of disease biomarkers from high-throughput data.
The recent advances in high-throughput data acquisition have driven a revolution in the study of human disease and determination of molecular biomarkers of disease states. It has become increasingly clear that many of the most important human diseases arise as the result of a complex interplay between several factors including environmental factors, such as exposure to toxins or pathogens, diet, lifestyle, and the genetics of the individual patient. Recent research has begun to describe these factors in the context of networks which describe relationships between biological components, such as genes, proteins and metabolites, and have made progress towards the understanding of disease as a dysfunction of the entire system, rather than, for example, mutations in single genes. We provide a summary of some of the recent work in this area, focusing on how the integration of different kinds of complementary data, and analysis of biological networks and pathways can lead to discovery of robust, specific and useful biomarkers of disease and how these methods can help shed light on the mechanisms and etiology of the diseases being studied.
Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA. Jason.McDermott@pnl.gov
Jason E
JE
McDermott
Michelle
M
Costa
Derek
D
Janszen
Mudita
M
Singhal
Susan C
SC
Tilton
D015415:Biomarkers / Q000032:analysis*; D003933:Diagnosis; D006801:Humans; D049490:Systems Biology
D015415:Biomarkers