Quantifying wellness and disease with personal, dense, dynamic data clouds

Abstract

Precision Medicine, where medical treatment is guided by deep molecular knowledge of the individual, has gained momentum in recent years. Rapid advancement in biological measurement technologies such as genome sequencing, mass spectrometry, protein capture assays, microfluidics and quantified-self devices provide an unprecedented opportunity to quantify, explain, and affect each person's health. The key challenge now is how to utilize these new capabilities to maximize wellness and prevent disease. These developments are concurrent with and aided by the increased availability of robust data analytic tools and cheap, scalable computation. In this dissertation, I present three steps taken to advance Precision Medicine. I present the first large multi-omic wellness study, where information from these -omics were integrated and used to provide personalized wellness guidance through a trained wellness coach. I present a holistic and modifiable wellness marker based on aging, generated from longitudinal multi-omic data. Finally, I apply systems approaches with dense phenotypic longitudinal data to profiling cancer, highlighting one approach to personalized 'N of 1' medicine. The research I present in this dissertation has led to the formation of two companies, so far.