# A Linked Data Application for Harmonizing Heterogeneous Biomedical Information
## 4 The Integration and Harmonization Process
### Layer 5: Drug Interactions  
NENs are biologically heterogeneous and contain subpopulations of cells with different angiogenic, invasive, and metastatic properties. As their response to therapeutic agents is equally heterogeneous, their treatment still represents an important clinical problem [33]. Understanding drugs' effects on NENs has been importantly investigated in the last years also using in vitro studies that have been essential to clarifying drug mechanism of action. Some innovative therapeutic options are also based on the study of the molecular pathways involved in the development and growth of NENs. In this context, the research has recently focused on the so-called _druggable genome_ that is, genes and gene products known or expected to interact with bioavailable compounds. In addition to the presence of a protein structure that can be powerfully bound by small molecules, good potential targets are proteins for which modulation of biological function could provide therapeutic benefits for the patient. Targeted therapy has proven to be a \begin{table}
\begin{tabular}{l l l} \hline \hline
**Field** & **Description** & **Example** \\ \hline gene & Gene identifier (_Ensembl_ taxonomy) & ENSG00000000003 \\ gene-name & Gene identifier (_HGNC_ taxonomy) & TSPAN6 \\ tissue & Name of a human tissue (controlled vocabulary of 44 terms) & breast \\ cell-type & Type of cells of the selected human tissue (controlled vocabulary) & glandular cells \\ level & Expression value of the gene within the tissue (_not detected, low, medium, high_) & high \\ reliability & Reliability of the expression value based on the evaluation of available data & approved \\  & and literature (_enhanced, supported, approved, uncertain_) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The main fields of the _HPA Normal Tissue Data Archive_. Figure 4: Relevant classes and relations of the additional layer 4.  
successful strategy in oncology, with the introduction of new therapeutic agents, including monoclonal antibodies and small molecule kinase inhibitors [34]. Following this trend, whereas the previous levels of the model offer researchers the ability to find mutated or altered genes implicated in NENs, the last level is designed to provide them with information on compounds and drugs that show documented interactions with these genes. The main external information sources integrated into this level are _ChEMBL_ and _DGldb_ (see Section 3.2) describing, respectively, drugs and drug-gene interactions. Table 5 shows the main fields of the _DGldb Interactions Archive_. It is a tabular TSV file where each row represents a documented interaction between a drug and a gene. We filtered this information (made of more than 85,000 associations) to consider only interactions with genes already included in the model. The standardized _HGNC_ (Human Genome Organization Gene Nomenclature Committee) gene name was used to associate the correct _Gene_ class from layer 2. Instead, the _drug-concept-id_ field was used to link the right instance of the _Substance_ class from the _ChEMBL_ ontology. To harmonize the filtered information with the multi-layered knowledge model, an ontological representation of drug-gene interaction is created as represented in Figure 5