Personalized Medicine

Drug targets for which there is human data (e.g., genetics) that links them to the disease are more likely to successfully complete clinical development and be approved as new drugs. However, the surmountable challenge of assembling large scale human cohorts has limited the collection of such data to national health organizations, and even these cohorts provide limited phenotyping and omics data due to the high cost of the tests.

To address this challenge, we initiated Project 10K, a large-scale, longitudinal, deeply phenotyped, multi-omics human cohort, that our lab is collecting. We aim to find novel diagnostic, prognostic, and therapeutic biomarkers for diseases, based on applying state of the art machine learning methods to deep phenotypic and multi-omics measurements of 10,000 human volunteers over a 10-year period.

The goals of Project 10k include:

  • Create the most deeply phenotyped human cohort globally
  • Develop personalized algorithms that accurately predict the likelihood of a person to developing a particular condition or disease within 5-10 years
  • Obtain molecular characterization of diseases on several multi-omics levels
  • Identify novel disease therapeutic targets
  • Develop machine learning algorithm and tools to model disease continuum and progression

To obtain a broad phenotypic view of the study participants we profile participants at unprecedented depth, including physiological assessment of their muscular, skeletal, liver, blood, heart, vascular, immune, gastrointestinal, and cognitive body systems using state of the art methods. We couple these measurements with molecular methods that profile the genetics, microbiome, metabolomic, transcriptomic, proteomic, and immune systems of each participant.

We apply machine learning methods to both the baseline variation in disease risk and the longitudinal data in order to identify novel therapeutic targets as well as means to modulate them by dietary, lifestyle, microbiome, and small molecules. We also devise algorithms for predicting future onset of various diseases based on baseline measurements.