A master pipeline for discovery and validation of biomarkers

Abstract

A major challenge in precision medicine is the development of biomarkers which can effectively guide patient treatment in a manner which benefits both the individual and the population. Much of the difficulty is the poor reproducibility of existing approaches as well as the complexity of the problem. Machine learning tools with rigorous statistical inference properties have great potential to move this area forward. In this chapter, we review existing pipelines for biomarker discovery and validation from a statistical perspective and identify a number of key areas where improvements are needed. We then proceed to outline a framework for developing a master pipeline firmly grounded in statistical principles which can yield better reproducibility, leading to improved biomarker development and increasing success in precision medicine.