Networks allow for visualizing and analyzing interactions between entities in complex systems, e.g., people in social networks or genes in biological networks. Networks are modeled as graphs with a given set of nodes and edges. The nodes represent the investigated entities, while the edges represent the relationships between these entities. Based on this, networks are utilized to predict the response to perturbations of one or more nodes or edges.
Biological systems are very complex, with thousands of genes, proteins, and other molecules interacting in a directed or undirected manner. These systems can be modeled with networks . Different types of analyses of the biological system can be done based on the networks, such as predicting the system’s response to a drug [2-4]. In vivo and in vitro testing of drugs and their combination is costly and time-consuming. Therefore, in silico testing of these drugs and consequently reducing the number of potential drugs for a specific disease is beneficial for medical research. The administration of drugs causes perturbations of the underlying system that can be analyzed using the representative biological network. Several different information layers, i.e., omics (genomics, proteomics, and metabolomics) levels, can be utilized for drug response prediction.