Protocol

Systems Medicine

Volume 1386 of the series Methods in Molecular Biology pp 353-374

Network-Assisted Disease Classification and Biomarker Discovery

  • Sonja StrunzAffiliated withBiomathematics and Bioinformatics Unit, Leibniz-Institute for Farm Animal Biology (FBN), Institute of Genetics and Biometry
  • , Olaf WolkenhauerAffiliated withDepartment of Systems Biology and Bioinformatics, University of RostockStellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University
  • , Alberto de la FuenteAffiliated withBiomathematics and Bioinformatics Unit, Leibniz-Institute for Farm Animal Biology (FBN), Institute of Genetics and Biometry Email author 

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput “omics” data is an active research topic in the field of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identified from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identification. In this chapter, after giving a general introduction to the problem of disease classification and biomarker discovery, we will review two types of network-assisted approaches: (1) approaches inferring activity scores for specific pathways which are subsequently used for classification and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.

Key words

Biomarker discovery Classification Feature selection Pathways Molecular networks