Paper Info
Title
Linked Data for Life Sciences.
Abstract
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.
Year
DOI
Venue
2017
10.3390/a10040126
ALGORITHMS
Keywords
Field
DocType
linked data,FAIR principles,meta-analysis,systematic review,knowledge discovery,semantic web
Data science,Body of knowledge,World Wide Web,Knowledge integration,Research question,Computer science,Interoperability,Linked data,Semantic Web,Knowledge extraction,Data model
Journal
Volume
Issue
ISSN
10
4
1999-4893
Citations 
PageRank 
References 
0
0.34
7
Authors
2
Name
Order
Citations
PageRank
Amrapali Zaveri136824.37
Gökhan Ertaylan200.34