Network-Guided Biomarker Discovery
This work tackles the problem of biomarker discovery for precision medicine, but it is a review paper, so it is incremental in summarizing existing methods rather than presenting new research.
The paper reviews three families of feature selection methods that integrate prior knowledge from biological networks to address the challenge of identifying biomarkers in whole-genome data, which typically has many more features than samples.
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.