# Linked Data for Life Sciences
## 5 Discussion  
Deployment of Linked Data in Life Sciences requires a consistent, functioning, and overarching framework that is able to integrate various steps of the research cycle. We have identified three fundamental tiers of the research cycle in life sciences (conducting systematic reviews, meta-analysis of existing data, and knowledge discovery of novel links across evidence streams and databases) that would greatly profit from the Linked Data implementation. The systematic review and meta-analysis (SR-MA) of available data from comparable studies of interest has been proven to be a very powerful tool, especially in epidemiology (clinical trials) and medicine (effects of medical interventions) [31], for confidently estimating the role of various exposures to different outcomes. Although there are success stories [32; 33], the main challenges in the meta-analysis of existing data lies in the accessibility of the study data and its interoperability [14]. Over the last decade, there have been numerous efforts to improve the quality of reporting for meta-analyses for diagnostic, randomized, and non-randomized study designs and observational biomedical research studies by establishing guidelines, checklists, and standards [34; 35; 36; 37]. However, none of the reporting guidelines fully incorporate the full set of details required for the conduct of SR-MAs [38; 39; 40] One example is that of the CONSORT (Consolidated Standards of Reporting Trials) statement, which is a checklist developed to tackle the problems arising from inadequate reporting of Randomized Control Trials. However, this checklist only includes 22 of the 100 items needed for successfully conducting SR-MAs [41; 42]. The use of Semantic Web technologies and Linked Data, specifically ontologies, has been increasingly applied in several life science use cases. In our previous work [41], we proposed a Center of Excellence in Research Reporting in Neurosurgery (CERR-N) and the creation of a clinically significant computational ontology to encode Randomized Controlled Trial (RCT) studies, specifically in neurosurgery. The study showed that standardized reporting for neurosurgery articles can be reliably achieved through the integration of a computational ontology within the context of a CERR-N. A similar approach can be applied for our particular use cases and further expanded to assist in meta-analysis and knowledge discovery. In fact, a number of studies have explored and successfully demonstrated the advantages of ontology-driven meta-analysis [42; 43]. The process of annotating articles with ontologies can be a tedious one, if it has to be done manually. In February 2008, the FEBS editorial board designed an experiment asking authors to provide structured information on protein interactions along the lines of the minimum information requirement for reporting protein interaction experiments (MIMx) recommendations [44]. However, the author input was of poor quality, which, in turn, burdened the curators, as they had to invest a lot of time into cleaning the input. The conclusion was that having an automated system that would provide a list of relevant identifiers would be beneficial. This would likely help to save authors and curators a significant amount of time. To complement this study, the BioCreative ([[http://www.biocreative.org/](http://www.biocreative.org/)]([http://www.biocreative.org/](http://www.biocreative.org/))) organizers challenged text-mining researchers to reproduce a subset of the annotations provided by the Structured Digital Abstracts. This study concluded that text mining could help to select specific articles for database curators (highest accuracy = 92%). Recently, to automatically annotate articles using concepts from existing ontologies, there is the BioPortal annotator ([[https://bioportal.bioontology.org/annotator](https://bioportal.bioontology.org/annotator)]([https://bioportal.bioontology.org/annotator](https://bioportal.bioontology.org/annotator))) tool.