# Linked Data for Life Sciences
## 4 Use Cases
### Knowledge Discovery  
After performing meta-analysis of the data extracted from various studies to confirm or refute the mechanistic link as the mechanism linking obesity to breast cancer, we want to investigate existing compounds or drugs that are either designed to act on the mechanism directly or effect the mechanism indirectly, where information is available in different data sources. This allows us to discover compounds that can be tested experimentally or by clinical trials to potentially alleviate the effect of the exposure (obesity) on the outcome (breast cancer).  
In searching for compounds that have an effect on IGF1R, we first access the connectivity map ([[https://portals.broadinstitute.org/cmap/](https://portals.broadinstitute.org/cmap/)]([https://portals.broadinstitute.org/cmap/](https://portals.broadinstitute.org/cmap/))) [29] or CancerDR ([[http://crdd.osdd.net/raghava/cancerdr/](http://crdd.osdd.net/raghava/cancerdr/)]([http://crdd.osdd.net/raghava/cancerdr/](http://crdd.osdd.net/raghava/cancerdr/))) [30] to analyze the existing data on more than 7000 expression profiles representing 1309 compounds in the connectivity map or 148 anticancer drugs and their effectiveness against around 1000 cancer cell lines, respectively. After identifying candidate compounds, the final step is to assess their suitability as a follow-up study, which requires the extraction of the PubChem Compound Identifier, DrugBank ID, and/or CheBI IDs of the compounds to search in various databases for reported efficacy and adverse effects.  
In our proposed framework, with all the biologically relevant data sets available as Linked Data (specifically biological data sets as part of the Bio2 RDF project) we can perform this analysis by querying the data sets and traversing the interlinks between them. For example, by querying Bio2 RDF, we can extract the IDs from PubChem, DrugBank, and ChEBI directly in one SPARQL query along with the associated information from each of these data sources. Listing 4 shows an example SPARQL query for the following question: \"Find all direct interactions of IGF1R protein from STRING-db andgive me all the compounds that are described to have DrugBank interactions with IGF1R or any other interacting partners.\"  
**Listing 4.** An example SPARQL query to retrieve all direct interactions of the Insulin-Like Growth Factor 1 Receptor (IGF1R) protein from STRING-db and all the compounds that are described to have DrugBank interactions with IGF1R or any other interacting partners.  
PREFIX drugbank: <[http://bio2 rdf.org/drugbank_vocabulary](http://bio2 rdf.org/drugbank_vocabulary):>  
PREFIX rdfs: <[[http://www.w3.org/2000/01/rdf-schema#](http://www.w3.org/2000/01/rdf-schema#)]([http://www.w3.org/2000/01/rdf-schema#](http://www.w3.org/2000/01/rdf-schema#))>  
PREFIX ex: <[[http://example.com/](http://example.com/)]([http://example.com/](http://example.com/))>  
SELECT?interaction?compound  
?protein a ex:Protein.  
?protein has:interaction?interaction.  
?compound a ex:Compound.  
?compound has:interaction?interaction.  
?interaction a drugbank:Drug-Drug-Interaction.  
FILTER regex(?protein, \"IGF1R\")  
Additionally, by making the data as well as the metadata and the results FAIR, the methodology and the results can be reproduced for any other similar use case. Also, exposing the data using the smartAPI specification will facilitate the finding and exploring of connections pertaining to the use case, especially those that were unknown before and could not have been discovered.