title: Merging genomic and phenomic data for research and clinical impact.
abstract: Driven primarily by advances in genomics, pharmacogenomics and systems biology technologies, large amounts of genomic and phenomic data are today being collected on individuals worldwide. Integrative analysis, mining, and computer modeling of these data, facilitated by information technology, have led to the development of predictive, preventive, and personalized medicine. This transformative approach holds the potential inter alia to enable future general practitioners and physicians to prescribe the right drug to the right patient at the right dosage. For such patient-specific medicine to be adopted as standard clinical practice, publicly accumulated knowledge of genes, proteins, molecular functional annotations, and interactions need to be unified and with electronic health records including phenotypic information, most of which still reside as paper-based records in hospitals. We review the state-of-the-art in terms of electronic data capture and medical data standards. Some of these activities are drawn from research projects currently being performed within the European Virtual Physiological Human (VPH) initiative; all are being monitored by the VPH INBIOMEDvision Consortium. Various ethical, legal and societal issues linked with privacy will increasingly arise in the post-genomic era. This will require a closer interaction between the bioinformatics/systems biology and medical informatics/healthcare communities. Planning for how individuals will own their personal health records is urgently needed, as the cost of sequencing a whole human genome will soon be less than U.S. $100. We discuss some of the issues that will need to be addressed by society as a result of this revolution in healthcare.
authors.affiliation: Centre for Computational Science, University College London, United Kingdom. n.shublaq@ucl.ac.uk
authors.forename: Nour W
authors.initials: NW
authors.lastname: Shublaq
authors.forename: Peter V
authors.initials: PV
authors.lastname: Coveney
mesh_terms: D019295:Computational Biology / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods; D057286:Electronic Health Records / Q000458:organization & administration; D015894:Genome, Human; D023281:Genomics / Q000458:organization & administration*; D006801:Humans; D008491:Medical Informatics Applications; D010641:Phenotype; D057285:Precision Medicine / Q000379:methods