Humanome: A Human Is A Data Repository

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The Humanome is made up of two parts, ‘Human’ and the suffix ‘-ome’. The suffix ‘-ome’ is related to the totality of a subject and here the subject is the health of a human. The Humanome model is a personal health profile and a data repository. It is based on data that influence personal health parameters from both public and private-sector sources, all of which affect health status. In this model, data is not divided into sections in order to avoid creating silos, but rather it is grouped into interconnected flows, which is why the data flows pictured above surround the individual placed at the centre.

Four categories of health data

  • Data on personal behaviour comes from existing data sources such as profiling, self reporting, data from wearables and other trackers, social and other media exposure, and engagement; as well as data sources of the near future, such as AR, VR and exogames.
  • Data on biology will come from a personal genomic profile (via sequencing), other adoptions of sequencing such as non-invasive parental testing (NIPT), and from classical biomarkers (molecular, histologic, radiographic or physiological), as well as new biomarkers of the -omics (transcriptomics, proteomics, metabolomics and microbiomics).
  • Societal factors includes at data points like employment, demographics, and education; in the near future this might also include blockchain-based individual monitoring systems.
  • Environmental data covers data points such as climate, pollution and noise which come from environmental impact assessments of geolocations, environmental seasonal dynamics & climate change projections; and in the near future might come from environmental wearables and environmental modelling.

The Humanome is not meant to be a complete list of existing types of data, but rather to serve as an inspiration for what types of data are already available both from the human body and its surroundings. All data flows within the Humanome must comply with specific data controls (e.g., interoperability and safety of data) as well as data contracts (e.g., on consent, or the secondary use of data). Such safety measures are crucial for establishing trust between individuals and institutions.