Pang C, van Enckevort D, de Haan M Kelpin F, Jetten J, Hendriksen D, de Boer T, Charbon B, Winder E, van der Velde KJ, Doiron D, Fortier I, Hillege H, Swertz MA
Bioinformatics 15;32(14):2176-83, July 2016
While the size and number of biobanks, patient registries and other data collections are increasing, biomedical researchers still often need to pool data for statistical power, a task that requires time intensive retrospective integration.
To address this challenge, we developed MOLGENIS/connect, a semi-automatic system to find, match and pool data from different sources. The system shortlists relevant source attributes from thousands of candidates using ontology-based query expansion to overcome variations in terminology. Then it generates algorithms that transform source attributes to a common target DataSchema. These include unit conversion, categorical value matching and complex conversion patterns (e.g. calculation of BMI). In comparison to human-experts, MOLGENIS/connect was able to auto-generate 27% of the algorithms perfectly, with an additional 46% needing only minor editing, representing a reduction in the human effort and expertise needed to pool data.
AVAILABILITY AND IMPLEMENTATION:
Source code, binaries and documentation are available as open-source under LGPLv3 from http://github.com/molgenis/molgenis and www.molgenis.org/connect