MOLGENIS/connect: a system for semi-automatic integration of heterogeneous phenotype data with applications in biobanks

Chao Pang, David van Enckevort, Mark de Haan, Fleur Kelpin, Jonathan Jetten, Dennis Hendriksen, Tommy de Boer, Bart Charbon, Erwin Winder, K. Joeri Velde, van der, Dany Doiron, Isabel Fortier, Hans Hillege, Morris A. Swertz*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
55 Downloads (Pure)

Abstract

Motivation: 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. Results: 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.

Original languageEnglish
Pages (from-to)2176-2183
Number of pages8
JournalBioinformatics
Volume32
Issue number14
DOIs
Publication statusPublished - 15-Jul-2016

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