Importance of the field: The global, integrated analysis of large-scale data sets encoding different levels of biological information opens up new possibilities to discover new biomarkers and elucidate complex mechanisms driving health and disease. Areas covered in this review: This article reviews fundamental systems approaches and applications for biomarker discovery in different biomedical domains. It introduces key challenges and requirements for the development of advanced computational techniques, resources and applications. It discusses how these approaches can fill in some of the current gaps in traditional biomarker discovery and disease classification. What the reader will gain: The reader will be introduced to recent advances, techniques and applications of systems approaches to biomarker discovery and disease classification. The reader will learn fundamental research principles and tasks required in the implementation of these approaches and applications. The reader will gain a better understanding of the role of systems biology, as well as of potential opportunities and advances. Take home message: Systems approaches to biomarker discovery may contribute to the discovery of more accurate and robust predictors of disease and clinical responses. Moreover, they can provide new and deeper clues of potential causal mechanisms underpinning physiological and pathological conditions.
- Biological networks
- Network-based disease classification
- Systems biology
- Translational bioinformatics