# Algorithms for Drug Sensitivity Prediction
## 5 Biological Analysis
This procedure can be conducted using platforms, such as the Search Tool for the Retrieval of Interacting Genes (STRING) [133] or Cytoscape [134]. To illustrate the biological relevance of features selected by a predictive model, we will consider the NCI-DREAM drug sensitivity prediction challenge where five different data types of gene expression (microarray data), methylation, RNA sequencing, RPPA and SNP6 are considered to generate individual random forest models for each subtype that are then combined together to generate the integrated prediction [48]. For our current analysis, we used the random forest variableimportance measure to generate the top 50 variables for each model built from an individual data type. Each individual set was used as input to the STRING ([[http://string-db.org/](http://string-db.org/)]([http://string-db.org/](http://string-db.org/))) database to analyze the Protein-Protein Interactions (PPI). The enrichment p-values for each of the five PPI networks are as follows: gene expression (0.508), methylation (0.840), RNA-Seq (0.059), RPPA (0) and SNP6 (0.319). The lowest \(p\)-value for RPPA shows that the important features obtained from the RPPA-generated random forest are significantly enriched as compared to random sub-networks. We passed the RPPA and gene expression output networks to Cytoscape to use the CyTargetLinker [135] application. CyTargetLinker extends the output network from STRING using Regulatory Interaction Networks (RegINs), which are local database files, to extend the network with the possible interactions: drugs, genes, mRNA, additional transcription factors and other targets. The RegIN files can be obtained from the CyTargetLinker website ([[http://projects.bigcat.unimaas.nl/cytargetlinker/](http://projects.bigcat.unimaas.nl/cytargetlinker/)]([http://projects.bigcat.unimaas.nl/cytargetlinker/](http://projects.bigcat.unimaas.nl/cytargetlinker/))). The DrugBank database is used to identify drug interactions containing 7759 compounds (drugs), some FDA approved and some experimental. DrugBank ([[http://www.drugbank.ca/](http://www.drugbank.ca/)]([http://www.drugbank.ca/](http://www.drugbank.ca/))) combines comprehensive pharmacological and pharmaceutical drug data along with the corresponding targets, sequences, structure and pathway information. Figure 5 shows the interactions among the top 50 features selected by gene expression and plotted using the visualization platform Cytoscape. Only nine out of these 50 (denoted by the red circles) had known interactions. The connections shown by the violet squares are the drug interactions obtained from the DrugBank database. On the other hand, when we observe the known prior connections among the top 50 features selected using the RPPA-based model, a significantly higher number of connections were identified, as shown in Figure 6. For RPPA-based top features, there were 41 proteins found (red circles) to interact with 434 drugs (violet squares). These forms of visualization displaying different types of interactions provide insights upon the biological relevance of the selected variables. The predictive performance of a model is one aspect of the goodness of fit of a model, whereas the biological relevance of the selected features is an equally important aspect of a predictive model. Figure 5: Protein and drug interactions among top features selected in gene expression data. The top 50 genes selected from gene expression data are passed to the STRING database to find protein-protein interaction. Then, this new network is passed to Cytoscape using the CyTargetLinker App to extend the Protein-Protein (PP) interactions in a 7759 drug database (DrugBank); only the elements marked with an * are displayed in this network.