Session Introduction

During January 2015, President Obama announced the Precision Medicine Initiative [1], strengthening communal efforts to integrate patient-centric molecular, environmental, and clinical “big” data. Such efforts have already improved aspects of clinical management for diseases such as non-small cell lung carcinoma [2], breast cancer [3], and hypertrophic cardiomyopathy [4]. To maintain this track record, it is necessary to cultivate practices that ensure reproducibility as large-scale heterogeneous datasets and databases proliferate. For example, the NIH has outlined initiatives to enhance reproducibility in preclinical research [5], both Science [6] and Nature [7] have featured recent editorials on reproducibility, and several authors have noted the issues of utilizing big data for public health [8], but few methods exist to ensure that big data resources motivated by precision medicine are being used reproducibly. Relevant challenges include: (1) integrative analyses of heterogeneous measurement platforms (e.g. genomic, clinical, quantified self, and exposure data), (2) the tradeoff in making personalized decisions using more targeted (e.g. individual-level) but potentially much noisier subsets Pacific Symposium on Biocomputing 2016