  Predictive modeling based on genomic data has gained popularity in biomedical
research and clinical practice by allowing researchers and clinicians to
identify biomarkers and tailor treatment decisions more efficiently. Analysis
incorporating pathway information can boost discovery power and better connect
new findings with biological mechanisms. In this article, we propose a general
framework, Pathway-based Kernel Boosting (PKB), which incorporates clinical
information and prior knowledge about pathways for prediction of binary,
continuous and survival outcomes. We introduce appropriate loss functions and
optimization procedures for different outcome types. Our prediction algorithm
incorporates pathway knowledge by constructing kernel function spaces from the
pathways and use them as base learners in the boosting procedure. Through
extensive simulations and case studies in drug response and cancer survival
datasets, we demonstrate that PKB can substantially outperform other competing
methods, better identify biological pathways related to drug response and
patient survival, and provide novel insights into cancer pathogenesis and
treatment response.
