Linked Data for Life Sciences

Amrapali Zaveri*, Gokhan Ertaylan*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Issue number4
Publication statusPublished - Dec 2017


  • linked data
  • FAIR principles
  • meta-analysis
  • systematic review
  • knowledge discovery
  • semantic web

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