Network-Guided Biomarker Discovery

TL;DR

This paper reviews methods that incorporate biological network information into feature selection to improve biomarker discovery from high-dimensional genomic data, addressing the limitations of classical approaches.

Contribution

It provides a comprehensive review of three families of network-guided feature selection methods for biomarker discovery.

Findings

Network-guided methods improve feature selection power.

Integration of prior biological knowledge enhances biomarker identification.

Review covers three main families of network-based approaches.

Abstract

Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnostic, prognostic and treatment choice to individual characteristics of a patient. In machine learning terms, biomarker discovery can be framed as a feature selection problem on whole-genome data sets. However, classical feature selection methods are usually underpowered to process these data sets, which contain orders of magnitude more features than samples. This can be addressed by making the assumption that genetic features that are linked on a biological network are more likely to work jointly towards explaining the phenotype of interest. We review here three families of methods for feature selection that integrate prior knowledge in the form of networks.