# Linked Data for Life Sciences
## 4 Use Cases
### Systematic Reviews  
Today, conventional search for scientific papers in order to perform systematic reviews includes searching for articles annotated with specific keywords. The authors choose these keywords when they submit their paper. MEDLINE uses a hierarchical structure called MeSH subject terms to annotate each article and continuously manually curates every new article published in the journals that are included in MEDLINE. This manual curation is quite tedious and time consuming, and the searches often exclude the article from MEDLINE journals that have not yet been fully indexed (new articles), as well as other PubMed citations that are not indexed for MEDLINE. In this manuscript, we demonstrate the premise of LD with a real-world example that we have recently published on the systematic analysis of the body of evidence linking obesity to breast cancer. The detailed methodology can be found in the original paper [26]. In summary, the conventional identification of relevant mechanisms linking obesity to breast cancer includes:  
* Identifying MeSH terms that are sufficient for describing the exposure (obesity) and the outcome (breast cancer) independently. * Identifying candidate mechanistic MeSH terms that could be the link between the exposure and the outcome. * Performing two independent searches: one with the exposure and one with the outcome to retrieve all the articles with all their metadata and associated MeSH terms. * Performing an enrichment analysis on the candidate mechanistic MeSH terms with the results of these two searches to rank the candidate MeSH terms for their association with the exposure and/or the outcome according to their preferential association in both exposure and outcome. The top candidate mechanistic link and all associated studies are then downloaded to be further studied for assessing the overall body of evidence linking obesity to the mechanistic link (first subreview) and the mechanistic link to the breast cancer (second subreview). Next steps in the systematic review framework entail extracting and coding the information (human studies/animal models/cell models) from the papers manually by independent researchers, and comparing the resulting documents with a scheme such as the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) assessment [27] to evaluate the quality of the body of evidence per evidence stream (i.e., human and animal studies) within each subreview. This systematic review process aims to integrate the body of knowledge spread over hundreds of manuscripts in various databases (findability), some behind paywalls (accessibility), and in different formats (interoperability), to test the merits of a specific scientific hypothesis. However, the sheer volume of literature in combination with the heterogeneity of the types of studies (human/animal/cell) and methods being used in those studies hinders its reproducibility [26; 28]. Our proposal is to use ontologies as a method for creating reporting standards for research articles. Ontologies provide structured vocabularies, terminologies, and reasoning capabilities that enable standardized and semantically interconnected machine-readable sections in a research article. Annotating individual facts in articles using an ontology can enable and speed up the search for and retrieval of relevant articles, as well as ensure that all related articles are included. Additionally, this facilitates the integration of the sparse data that is currently been stored in different locations and formats in a central repository. Then, by storing all the articles annotated using ontologies in a central repository/triple store, this will address the problem of finding and retrieving relevant articles by performing a single query. Another problem is dealing with the sheer volume of the data. That is, in our use case, when we query repositories using MeSH terms, we tend to retrieve a huge number of articles linked more to the parent class (e.g., neoplasms vs breast neoplasms).