Pathway aggregation for survival prediction via multiple kernel learning.

ABSTRACT: Attempts to predict prognosis in cancer patients using high-dimensional genomic data such as gene expression in tumor tissue can be made difficult by the large number of features and the potential complexity of the relationship between features and the outcome. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways and networks reduces the dimensionality of the problem and could improve prediction accuracy. Additionally, such knowledge-based models may be more biologically grounded and interpretable. Prediction could potentially be further improved by allowing for complex nonlinear pathway effects. The kernel machine framework has been proposed as an effective approach for modeling the nonlinear and interactive effects of genes

SUBMITTER: Sinnott JA 

PROVIDER: S-EPMC5994931 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature