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Linked Data for Life Sciences

1
Institute of Data Science, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
2
Maastricht Centre for Systems Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
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Authors to whom correspondence should be addressed.
Algorithms 2017, 10(4), 126; https://doi.org/10.3390/a10040126
Received: 27 September 2017 / Revised: 11 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
(This article belongs to the Special Issue Algorithmic Methods for Computational Molecular Biology)
Massive amounts of data are currently available and being produced at an unprecedented rate in all domains of life sciences worldwide. However, this data is disparately stored and is in different and unstructured formats making it very hard to integrate. In this review, we examine the state of the art and propose the use of the Linked Data (LD) paradigm, which is a set of best practices for publishing and connecting structured data on the Web in a semantically meaningful format. We argue that utilizing LD in the life sciences will make data sets better Findable, Accessible, Interoperable, and Reusable. We identify three tiers of the research cycle in life sciences, namely (i) systematic review of the existing body of knowledge, (ii) meta-analysis of data, and (iii) knowledge discovery of novel links across different evidence streams to primarily utilize the proposed LD paradigm. Finally, we demonstrate the use of LD in three use case scenarios along the same research question and discuss the future of data/knowledge integration in life sciences and the challenges ahead. View Full-Text
Keywords: linked data; FAIR principles; meta-analysis; systematic review; knowledge discovery; semantic web linked data; FAIR principles; meta-analysis; systematic review; knowledge discovery; semantic web
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Zaveri, A.; Ertaylan, G. Linked Data for Life Sciences. Algorithms 2017, 10, 126.

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