Massive amounts of data are currently available and being produced at an unprecedented\nrate in all domains of life sciences worldwide. However, this data is disparately stored and is in\ndifferent and unstructured formats making it very hard to integrate. In this review, we examine\nthe state of the art and propose the use of the Linked Data (LD) paradigm, which is a set of best\npractices for publishing and connecting structured data on the Web in a semantically meaningful\nformat. We argue that utilizing LD in the life sciences will make data sets better Findable, Accessible,\nInteroperable, and Reusable. We identify three tiers of the research cycle in life sciences, namely\n(i) systematic review of the existing body of knowledge, (ii) meta-analysis of data, and (iii) knowledge\ndiscovery of novel links across different evidence streams to primarily utilize the proposed LD\nparadigm. Finally, we demonstrate the use of LD in three use case scenarios along the same\nresearch question and discuss the future of data/knowledge integration in life sciences and the\nchallenges ahead.
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