Arjun K. Manrai1, Chirag J. Patel1, Nils Gehlenborg1, Nicholas P. Tatonetti2, John P.A. Ioannidis3, Isaac S. Kohane1
1Department of Biomedical Informatics, Harvard Medical School
2Department of Biomedical Informatics, Columbia University
3Department of Medicine, Stanford University School of Medicine
Email: Manrai@post.harvard.edu, Chirag_Patel@hms.harvard.edu, Nils@hms.harvard.edu, Nick.Tatonetti@columbia.edu, Jioannid@stanford.edu, Isaac_Kohane@hms.harvard.edu
Pacific Symposium on Biocomputing 21:180-182(2016)
© 2016 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
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 of data, and (3) the unprecedented scale of asynchronous observational and population- level inquiry (i.e. many investigators separately mining shared/publicly-available data).