Interoperability

Interoperability of systems is crucial to allow access to data and tools across systems and borders. While the initial focus has been on making genomic data interoperable, in order to make precision medicine a reality many more types of data sets will need to be integrated including other ’omics data, clinical and medical data, prescribing data, environmental exposure data sets as well as socio-economic and geographic information. This requires linking of the genomic medicine agenda to other digital transformations in the health system and beyond including adoption of electronic health records and the use of artificial or machine-learning technologies. 

Resource on Interoperability & Machine Learining

FHIR® – Fast Healthcare Interoperability Resources is a next generation standards framework created by HL7. hl7.org/fhir Section 10.8 Genomics Implementation Guidance https://www.hl7.org/fhir/genomics.html

Williams AM, Liu Y, Regner KR, Jotterand F, Liu P, Liang M. Artificial intelligence, physiological genomics, and precision medicine. Physiol Genomics. 2018;50(4):237-43. https://doi.org/10.1152/physiolgenomics.00119.2017

Leung MKK, Delong A, Alipanahi B, Frey BJ. Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets. Proceedings of the IEEE. 2016;104(1):176-97. https://doi.org/10.1109/JPROC.2015.2494198

Swaminathan R, Huang Y, Moosavinasab S, Buckley R, Bartlett CW, Lin SM. A Review on Genomics APIs. Comput Struct Biotechnol J. 2016;14:8-15. https://doi.org/10.1016/j.csbj.2015.10.004

Libbrecht MW, Noble WS. Machine learning in genetics and genomics. Nat Rev Genet. 2015;16(6):321-32. https://doi.org/10.1038/nrg3920