References
- FDA. Paving the Way for Personalized Medicine: FDA’s Role in the New Era of Medical Product Development; Available from: http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/PersonalizedMedicine/UCM372421.pdf; 1b. Data scientist: the sexiest job of the 21st century. Davenport TH, Patil DJ. Harvard Business Review, 2012. Available at: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ [Last accessed 28 Dec 2015].
- Morgan L. Big Data: 6 real life business cases; 2015 [Last accessed 17 Dec 2015]. Available from: http://www.informationweek.com/software/enterprise-applications/big-data-6-real-life-business-cases/d/d-id/1320590?image_number=1
- Laskowski N. Ten analytics success stories in a nutshell; 2015 [Last accessed 17 Dec 2015]. Available from: http://searchcio.techtarget.com/opinion/Ten-analytics-success-stories-in-a-nutshell
- Evans C. Precision medicine is already working to cure Americans: these are their stories; 2015 [Last accessed 17 Dec 2015]. Available from: https://www.whitehouse.gov/blog/2015/01/29/precision-medicine-already-working-cure-americans-these-are-their-stories
- Highnam G, Mittelman D. Personal genomes and precision medicine. Genome Biol. 2012;13:324.
- Duffy DJ. Problems, challenges, and promises: perspectives on precision medicine. Brief Bioinform. 2016;1–11. doi:10.1093/bib/bbv060.
- Stephens ZD, Lee SY, Faghri F, et al. Big data: astronomical or genomical? PLoS Biol. 2015;13:e1002195.
- Hu H, Wen Y, Chua T-S, et al. Toward scalable systems for big data analytics: a technology tutorial. IEEE Access. 2014;2:652–687.
- Precision Medicine Initiative. The White House; 2015 [Last accessed 17 Dec 2015]. Available from: https://www.whitehouse.gov/precision-medicine
- Institute of Medicine. Finding what works in health care. National Washington, DC: Academies Press, 2011 [cited 2013 Mar 1]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK209518/.
- Aronson SJ, Rehm HL. Building the foundation for genomics in precision medicine. Nature. 2015;526:336–342.
- Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38:65–76.
- Fan J, Han F, Liu H. Challenges of big data analysis. Natl Sci Rev. 2014;1:293–314.
- Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472:90–94.
- Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet. 2013;14:618–630.
- Porter JH, Hanson PC, Lin -C-C. Staying afloat in the sensor data deluge. Trends Ecol Evol. 2012;27:121–129.
- Zhang X, Wu T, Jiang T. A review of EEG and MEG for brainnetome research. Cogn Neurodyn. 2014;8:87–98.
- Yang J, Gong P, Fu R, et al. The role of satellite remote sensing in climate change studies. Nat Clim Chang. 2013;3:875–883.
- Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921.
- Zerhouni E. The NIH roadmap. Science. 2003;302:63–72.
- Green ED, Guyer MS. National human genome research institute. Charting a course for genomic medicine from base pairs to bedside. Nature. 2011;470:204–213.
- Collins FS, Wilder EL, Zerhouni E. NIH Roadmap/Common fund at 10 years. Science. 2014;345:274–276.
- Eisenstein M. Big data: the power of petabytes. Nature. 2015;527:S2–S4.
- Sboner A, Elemento O. A primer on precision medicine informatics. Brief Bioinform. 2015. doi:10.1093/bib/bbv032.
- Crawford DC, Crosslin DR, Tromp G, et al. eMERGEing progress in genomics – the first seven years. Front Genet. 2014;5:184.
- Verma SS, de Andrade M, Tromp G, et al. Imputation and quality control steps for combining multiple genome-wide datasets. Front Genet. 2014;5:370.
- Visscher PM, Brown MA, McCarthy MI, et al. Five years of GWAS discovery. Am J Hum Genet. 2012;90:7–24.
- Motsinger-Reif AA, Jorgenson E, Relling MV, et al. Genome-wide association studies in pharmacogenomics: successes and lessons. Pharmacogenet Genom. 2014;23:383–394.
- Lippert C, Listgarten J, Liu Y, et al. FaST linear mixed models for genome-wide association studies. Nat Methods. 2011;8:833–835.
- Korte A, Vilhjalmsson BJ, Segura V, et al. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet. 2012;44:1066–1071.
- Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44:821–824.
- Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16:321–332.
- Welter D, MacArthur J, Morales J, et al. The NHGRI GWAS catalog, a curated resources of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–D1006.
- EMBL-EBI. GWAS Catalog; 2015 [Last accessed 17 Dec 2015]. Available from: www.ebi.ac.uk/gwas
- Schrodi SJ, Mukherjee S, Shan Y, et al. Genetic-based prediction of disease traits: prediction is very difficult, especially about the future. Front Genet. 2014;5:162.
- Precision Medicine Initiative (PMI) Working Group Report to the Advisory Committee to the Director, NIH. The Precision Medicine Initiative Cohort Program – building a research foundation for 21st century medicine; 2015 [Last Accessed 17 Dec 2015]. Available from: https://www.nih.gov/sites/default/files/research-training/initiatives/pmi/pmi-working-group-report-20150917-2.pdf
- Greely HT. Get ready for the flood of fetal gene screening. Nature. 2011;469:289–291.
- Eichler EE, Flint J, Gibson G, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010; 11:446–450.
- Yang J, Benyamin B, McEvoy BP, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–569.
- Moser G, Lee SH, Hayes BJ, et al. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet. 2015;11:e1004969.
- ClinVar. National Center for Biotechnology Information; 2015 [Last accessed 17 Dec 2015]. Available from: https://www.ncbi.nlm.nih.gov/clinvar/
- Landrum MJ, Lee JM, Riley GR, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucl Acids Res. 2014;42:D980–D985.
- Purcell SM, Moran JL, Fromer M, et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature. 2014;506:185–190.
- Surakka I, Horikoshi M, Magi R, et al. The impact of low-frequency and rare variants on lipid levels. Nat Genet. 2015;47:589–597.
- Mancuso N, Rohland N, Rand KA, et al. The contribution of rare variation to prostate cancer heritability. Nat Genet. 2015. doi:10.1038/ng.3446.
- Chen R, Mias GI, Li-Pook-Than J, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148:1293–1307.
- Chen R, Snyder M. Systems biology: personalized medicine for the future? Curr Op Pharmac. 2012;12:623–628.
- Hood L, Lovejoy JC, Price ND. Integrating big data and actionable health coaching to optimize wellness. BMC Medicine. 2015;13:4.
- Diamandis EP. The hundred person wellness project and Google’s baseline study: medical revolution or unnecessary and potentially harmful over-testing? BMC Medicine. 2015;13:5.
- Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16:85–97.
- Sinha R, Abnet CC, White O, et al. The microbiome quality control project: baseline study design and future directions. Genome Biol. 2015;16:276.
- Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME) – towards standards for microarray data. Nat Genet. 2001;29:365–371.
- Blumenthal D. Stimulating the adoption of health information technology. New Engl J Med. 2009;360:1477–1479.
- Ben-Assuli O. Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments. Health Policy. 2015;119:287–297.
- Jensen AB, Moseley PL, Oprea TI, et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat Commun. 2014;5:4022.
- Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011;12:417–418.
- Flintoft L. Phenome-wide association studies go large. Nat Rev Genet. 2014;15:2.
- Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13:395–405.
- Khalifa A, Meystre S. Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes. J Biomed Inform. 2015;58S:S128–S132.
- Liao KP, Cai T, Savova GK, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015;350:h1885.
- Ryan PB, Madigan D, Stang PE, et al. Medication-wide association studies. CPT Pharmacometrics Syst Pharmacol. 2013;2:e76.
- Kukafka R, Ancker JS, Chan C, et al. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007;40:398–409.
- Newton KM, Peissig PL, Kho AN, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc. 2013;20:e147–e154.
- Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances and perspectives. J Am Med Inform Assoc. 2013;20:e206–e211.
- What is the Phenotype KnowledgeBase? Vanderbilt University; 2014 [Last accessed 17 Dec 2015]. Available from: https://www.phekb.org
- OHDSI. Observational Health Data Sciences and Informatics; 2015 [Last accessed 17 Dec 2015]. Available from: www.ohdsi.org
- FDA. Mini-Sentinel; 2014 [Last accessed 17 Dec 2015]. Available from: www.mini-sentinel.org
- Spiegel PK. The first clinical X-ray made in America – 100 years. Am J Roentgenology. 1995;164:241–243.
- Arenson RL, Andriole KP, Avrin DE, et al. Computers in imaging and health care: now and in the future. J Digit Imaging. 2000;13:145–156.
- Seto B, Friedman C. Moving toward multimedia electronic health records: how do we get there? J Am Med Inform Assoc. 2012;19:503–505.
- Hsu W, Markey MK, Wang MD. Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities. J Am Med Inform Assoc. 2013;20:1010–1013.
- Public Image Databases. Cornell University: Computer Vision and Image Analysis Group; 2009 [Last accessed 17 Dec 2015]. Available from: http://www.via.cornell.edu/databases/
- Aylward SR. Open-Access Medical Image Repositories; 2008 [Last accessed 17 Dec 2015]. Available from: http://www.aylward.org/notes/open-access-medical-image-repositories
- The ADHD-200 Consortium. The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci. 2012;6:62.
- Fonseca CG, Backhaus M, Bluemke DA, et al. The Cardiac Atlas project – an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics. 2011;27:2288–2295.
- Thompson PM, Stein JL, Medland SE, et al. The ENIGMA consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8:153–182.
- Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1046–1057.
- Tomczak K, Czerwinska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol. 2015;19:A68–A77.
- Clarke LP, Nordstrom RJ, Zhang H, et al. the quantitative imaging network: NCI’s historical perspective and planned goals. Transl Oncol. 2014;7:1–4.
- Smith S. 4 ways the IBM Watson is changing health care, from diagnosing disease to treating it. Medical Daily;2015 [Last accessed 18 Dec 2015]. Available from: http://www.medicaldaily.com/4-ways-ibm-watson-changing-health-care-diagnosing-disease-treating-it-364394
- Aaerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
- Bron EE, Smits M, Van Der Flier WM, et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage. 2015;111:562–579.
- Lee H, Chen Y-P P. Image based computer aided diagnosis system for cancer detection. Expert Syst Appl. 2015;42:5356–5365.
- Tokuda O, Harada Y, Ohishi Y, et al. Investigation of computer-aided diagnosis system for bone scans: a retrospective analysis in 406 patients. Ann Nucl Med. 2014;28:329–339.
- Waterton JC, Pylkkanen L. Qualification of imaging biomarkers for oncology drug development. European J of Cancer. 2012;48:409–415.
- Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med. 2015;7:283rv3.
- Burke LE, Ma J, Azar KMJ, et al. Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American heart association. Circulation. 2015;132:1157–1213.
- Donker T, Petrie K, Proudfoot J, et al. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013;15:e247.
- Clarke J, Proudfoot J, Birch M-R, et al. Effects of mental health self-efficacy on outcomes of a mobile phone and web intervention for mild-to-moderate depression, anxiety and stress: secondary analysis of a randomised controlled trial. BMC Psychiatry. 2014;14:272.
- Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data. 2013;1:85–99.
- Shih PC, Han K, Poole ES, et al. Use and adoption challenges of wearable activity trackers. iConference Proceedings 2015.
- Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA. 2015;313:459–460.
- Wearing your intelligence: how to apply artificial intelligence in wearables and IoT. WIRED Magazine; 2014 [Last accessed 17 Dec 2015]. Available from: http://www.wired.com/insights/2014/12/wearing-your-intelligence/
- Banaee H, Ahmed MU, Loutfi A. Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors. 2013;13:17472–17500.
- Brooks C. Introductory econometrics for finance. 3rd ed. Cambridge: Cambridge University Press; 2014.
- Fan Z, Dror RO, Mildorf TJ, et al. Identifying localized changes in large systems: change-point detection for biomolecular simulations. Proc Natl Acad Sci. 2015;112:1–6.
- Reis S, Seto E, Northcross A, et al. Integrating modelling and smart sensors for environmental and human health. Environ Modell Software. 2015; 74: 238–246.
- Ritchie MD, Denny JC, Crawford DC, et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet. 2010;86:560–572.
- Gottesman O, Kuivaniemi H, Tromp G, et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present and future. Genet Med. 2013;15:761–771.
- Regeneron and Geisinger Health System announce major human genetics research collaboration. Regeneron Pharmaceuticals, Inc.; 2014 [Last accessed 17 Dec 2015]. Available from: http://investor.regeneron.com/releasedetail.cfm?ReleaseID=818844
- Kvale MN, Hesselson S, Hoffman TJ, et al. Genotyping informatics and quality control for 100,000 subjects in the genetic epidemiology research on adult health and aging (GERA) cohort. Genet. 2015;200:1051–1060.
- Li L, Ruau DJ, Patel CJ, et al., et al. Disease risk factors identified through shared genetic architecture and electronic medical records. Sci Transl Med. 2014;6:234ra57.
- Li L, Cheng W-Y, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7:311ra174.
- Shen L, Thompson PM, Potkin SG, et al. Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers. Brain Imaging Behav. 2014;8:183–207.
- Stein JL, Hua X, Lee S, et al. Voxelwise genome-wide association study (vGWAS). NeuroImage. 2010;53:1160–1174.
- Hyde LW, Bogdan R, Hariri AR. Understanding risk for psychopathology through imaging gene-environment interactions. Trends Cogn Sci. 2011;15:417–427.
- Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidem. 2005;14:1847.
- Wild CP, Scalbert A, Herceg Z. Measuring the exposome: a powerful basis for evaluating environmental exposures and cancer risk. Environ Mol Mutagen. 2013;54:480–499.
- Sanchez FM, Gray K, Bellazzi R, et al. Exposome informatics: considerations for the design of future biomedical research information systems. J Am Med Inform Assoc. 2014;21:386–390.
- Bradley JR, Holan SH, Wikle CK. Mixed effects modeling for areal data that exhibit multivariate-spatio-temporal dependencies. Arxiv: 1407.7479v2. 2014. Available from: http://arxiv.org/abs/1407.7479
- Katzfuss M, Cressie N. Bayesian hierarchical spatio-temporal smoothing for very large datasets. Environmetrics. 2012;23:94–107.
- Poldrack RA, Laumann TO, Koyejo O, et al. Long-term neural and physiological phenotyping of a single human. Nat Comm. 2015;6:8885.
- Mendelsohn J. Personalizing oncology: perspectives and prospects. J Clin Oncol. 2013;31:1904–1911.
- Garraway LA, Verweij J, Ballman KV. Precision oncology: an overview. J Clin Oncol. 2013;31:1803–1805.
- Yu Q, Ding J. Precision cancer medicine: where to target? Acta Pharmacol Sin. 2015;36:1161–1162.
- Davies H, Bignell GR, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature. 2002;417:949–954.
- Flaherty KY, Puzanov I, Kim KB, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med. 2010;363:809–819.
- Lievre A, Bachet J-B, Le Corre D, et al. KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer. Cancer Res. 2006;66:3992–3995.
- Pao W, Miller V, Zakowski M, et al. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci. 2004;101:13306–13311.
- Lynch TJ, Bell DW, Sordella R, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 2004;350:2129–2139.
- Paez JG, Janne PA, Lee JC, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science. 2004;304:1497–1500.
- Politi K, Herbst RS. Lung cancer in the era of precision medicine. Clin Cancer Res. 2015;21:2213–2220.
- Fraser M, Berlin A, Bristow RG, et al. Genomic, pathological, and clinical heterogeneity as drivers of personalized medicine in prostate cancer. Urol Oncol-Semin Ori. 2015;33:85–94.
- Dawson S-J, Rueda OM, Aparicio S, et al. A new genome-driven integrated classification of breast cancer and its implications. Embo J. 2013;32:617–628.
- Enhance cancer diagnosis & treatment. ASCO CancerLinq; 2015 [Last accessed 17 Dec 2015]. Available from: www.cancerlinq.org
- Servant N, Romejon J, Gestraud P, et al. Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial. Front Genet. 2014;5:152.
- Burrell RA, McGranahan N, Bartek J, et al. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501:338–345.
- Du W, Elemento O. Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies. Oncogene. 2015;34:3215–3225.
- Jamal-Hanjani M, Hackshaw A, Ngai Y, et al. Tracking genomic cancer evolution for precision medicine: the lung TRACERx study. PLoS Biol. 2014;12:e1001906.
- Gajewski TF, Woo S-R, Zha Y, et al. Cancer immunotherapy strategies based on overcoming barriers within the tumor microenvironment. Curr Opin Immun. 2013;25:268–276.
- Jain RK. Normalizing tumor microenvironment to treat cancer: bench to bedside to biomarkers. J Clin Onc. 2013;31:2205–2218.
- Andre F, Mardis E, Salm M, et al. Prioritizing targets for precision cancer medicine. Ann Onc. 2014;25:2295–2303.
- Global Alliance for Genomics & Health. Global Alliance; 2015 [Last accessed 17 Dec 2015]. Available from: https://genomicsandhealth.org/
- Health Information Privacy. US Department of Health & Human Services; 2015 [Last accessed 17 Dec 2015]. Available from: http://www.hhs.gov/ocr/privacy/index.html
- Hazin R, Brothers KB, Malin BA, et al. Ethical, legal, and social implications of incorporating genomic information into electronic health records. Genet Med. 2013;15:810–816.
- Harrison MI, Koppel R, Bar-Lev S. Unintended consequences of information technologies in health care – an interactive sociotechnical analysis. J Am Med Inform Assoc. 2007;14:542–549.
- Hudson KL, Collins FS. Bringing the common rule into the 21st century. N Engl J Med. 2015;373:2293–2296.
- Hartzler A, McCarty CA, Rasmussen LV, et al. Stakeholder engagement: a key component of integrating genomic information into electronic health records. Genet Med. 2013;15:792–801.
- Kohane IS. Ten things we have to do to achieve precision medicine. Science. 2015;349:37–38.
- Education. American Society for Human Genetics; 2015 [Last accessed 17 Dec 2015]. Available from: http://www.ashg.org/education/
- Paton C. Massive open online course for health informatics education. Healthc Inform Res. 2014;20:81–87.
- Brazas MD, Lewitter F, Schneider MV, et al. A quick guide to genomics and bioinformatics training for clinical and public audiences. PLoS Comput Biol. 10: 2014; e1003510.
- Steinberg D, Horwitz G, Zohar D. Building a business model in digital medicine. Nat Biotech. 2015;33:910–920.
- Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med. 2012;366:489–491.
- Bourne PE, Lorsch JR, Green ED. Perspective: sustaining the big-data ecosystem. Nature. 2015;527:S16–S17.
- A community platform for NGS assay evaluation and regulatory science exploration. precisionFDA. U.S. Food and Drug Administration; 2015 [Last accessed 1 Dec 2016]. Available from: https://precision.fda.gov
- BaseSpace – Genomics Cloud Computing. Illumina, Inc.; 2015 [Last accessed 1 Dec 2016]. Available from: https://basespace.illumina.com/home/prep
- Marx V. Human phenotyping on a population scale. Nat Methods. 2015;12:711–714.
- 23andMe Research. 23andMe; 2015 [Last accessed 28 Dec 2015]. Available from: https://www.23andme.com/research/
- ResearchKit. Apple; 2015 [Last accessed 28 Dec 2015]. Available from: http://www.apple.com/researchkit/?cid=wwa-us-kwg-iphone-com
- Diehl P. BGI plans to sequence the world; 2013 [Last accessed 28 Dec 2015]. Available from: http://biotech.about.com/od/investinginbiotech/a/Bgi-Plans-To-Sequence-The-World.htm
- Unrivaled capabilities. deCODE genetics; 2015 [Last accessed 28 Dec 2015]. Available from: http://www.decode.com/research/
- The 100,000 Genomes Project. Genomics England; 2015 [Last accessed 28 Dec 2015]. Available from: https://medium.com/precision-medicine/the-genome-war-round-two-441c213e542#.9dbpdpu29
- Robison RJ. The Genome War, round two; 2015 [Last accessed 28 Dec 2015]. Available from: https://medium.com/precision-medicine/the-genome-war-round-two-441c213e542#.9dbpdpu29
- 100K Wellness Project. Institute of Systems Biology; 2015 [Last accessed 28 Dec 2015]. Available from: https://www.systemsbiology.org/research/100k-wellness-project/
- Arivale. Arivale; 2015 [Last accessed 1 Dec 2016]. Available from: www.arivale.com
- Bergen M. Verily, Google’s health gambit, is stacked with scientists. Now it needs to build a business. re/code; 2015 [Last accessed 28 Dec 2015]. Available from: http://recode.net/2015/12/14/verily-googles-health-gambit-is-stacked-with-scientists-now-it-needs-to-build-a-business/
- About BD2K: Data science at NIH. National Institutes of Health; 2015 [Last accessed 17 Dec 2015]. Available from: https://datascience.nih.gov/bd2k/about
- IMI 2. Innovative Medicines Initiative; 2010 [Last accessed 17 Dec 2015]. Available from: http://www.imi.europa.eu/content/imi-2
- About ONC. Newsroom, HealthIT.gov; 2014 [Last accessed 17 Dec 2015]. Available from: https://www.healthit.gov/newsroom/about-onc
- National Health Plan Collaborative. National Health Plan Collaborative; 2014 [Last accessed 17 Dec 2015]. Available from: http://nationalhealthplancollaborative.org/index.html
- Simons Center for Data Analysis. Simons Foundation; 2015 [Last accessed 17 Dec 2015]. Available from: https://www.simonsfoundation.org/simons-center-for-data-analysis/
- Welcome. American Society of Clinical Oncology; 2015 [Last accessed 17 Dec 2015]. Available from: http://www.asco.org/genetics-toolkit/welcome
- Stand up to cancer – this is where the end of cancer begins. Entertainment Industry Foundation; 2015 [Last accessed 17 Dec 2015]. Available from: http://www.standup2cancer.org/
- Annas GJ, Elias S. 23andMe and the FDA. N Engl J Med. 2014;370:985–988.
- Baudhuin LM. The FDA and 23andMe: violating the first amendment or protecting the rights of consumers?. Clin Chem. 2014;60:835–837.
- Williams MS. Is the genomic translational pipeline being disrupted?. Hum Genomics. 2015;9:9.
- Schork N. Personalized medicine: time for one-person trials. Nature. 2015;520:609–611.
- The public health evidence for FDA oversight of laboratory developed tests: 20 case studies. Office of Public Health Strategy and Analysis (FDA); [Last accessed 17 Dec 2015]. Available from: http://www.fda.gov/downloads/AboutFDA/ReportsManualsForms/Reports/UCM472777.pdf
- McKinsey Global Institute. Big data: the next frontier for innovation, competition, and productivity. June 2011.
- Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotech. 2012;29:613–624.
- NIMM overview – Health and social care information centre. NHS Infrastructure Maturity Model. Health & Social Care Information Centre; 2015 [Last accessed 21 Dec 2015]. Available from: http://systems.hscic.gov.uk/nimm/overview