PERSONALIZED MEDICINE: FROM GENOTYPES, MOLECULAR PHENOTYPES AND THE QUANTIFIED SELF, TOWARDS IMPROVED MEDICINE

Joel T. Dudley¹, Jennifer Listgarten², Oliver Stegle³, Steven E. Brenner⁴, Leopold Parts⁵

¹Icahn School of Medicine at Mount Sinai
²Microsoft Research
³European Molecular Biology Laboratory, European Bioinformatics Institute
⁴Department of Plant & Microbial Biology, 111 Koshland Hall, University of California
⁵University of Toronto, Donnelly Centre for Cellular and Biomolecular Research
Email: joel.dudley@mssm.edu, jennl@microsoft.com, oliver.stegle@ebi.ac.uk, brenner@compbio.berkeley.edu, leopold.parts@utoronto.ca

Pacific Symposium on Biocomputing 20:342-346(2015)

Abstract

Advances in molecular profiling and sensor technologies are expanding the scope of personalized medicine beyond genotypes, providing new opportunities for developing richer and more dynamic multi- scale models of individual health1,2. Recent studies demonstrate the value of scoring high-dimensional microbiome3, immune4, and metabolic5 traits from individuals to inform personalized medicine. Efforts to integrate multiple dimensions of clinical and molecular data towards predictive multi-scale models of individual health and wellness are already underway6-8. Improved methods for mining and discovery of clinical phenotypes from electronic medical records9 and technological developments in wearable sensor technologies present new opportunities for mapping and exploring the critical yet poorly characterized "phenome" and "envirome" dimensions of personalized medicine10,11. There are ambitious new projects underway to collect multi-scale molecular, sensor, clinical, behavioral, and environmental data streams from large population cohorts longitudinally to enable more comprehensive and dynamic models of individual biology and personalized health12. Personalized medicine stands to benefit from inclusion of rich new sources and dimensions of data. However, realizing these improvements in care relies upon novel informatics methodologies, tools, and systems to make full use of these data to advance both the science and translational applications of personalized medicine.