Structural and relational data mining for systems biology applications

Due to the enormous accumulation of experimental data and the increasing need for combining heterogeneous data sources, the field of systems biology yields novel and very interesting problems in data analysis. The development of high-throughput technologies has opened the possibility to study the behavior of many cellular components simultaneously. Therefore, there is an increasing interest and effort in not only understanding the functions of single isolated components, but also revealing the interactions and functional relationships between different components. Often, the outcome of large-scale measurements is conveniently represented in a structured form; prominent examples are proteinprotein interaction networks, coexpression networks for genes, and bipartite graphs of associations between experimental conditions and regulated genes. This thesis presents different methods that aim at finding interesting patterns in such data. The main contributions are as follows. First, an exact enumerative approach to dense cluster detection is proposed. Given a weighted interaction network and a default weight for missing edges, the density of a node set is defined as the average pairwise interaction weight. The described method finds all patterns that satisfy a user-defined minimum density threshold. Conceptually, this task is a generalization of clique search; however, the standard techniques to solve that problem are not appropriate for the generalized question. Fortunately, an efficient enumeration strategy can be achieved by adopting the reverse search paradigm. Remarkably, the same algorithmic framework is applicable to discover cluster patterns in other types of structured data, like asymmetric binary relations and multipartite graphs, as well as hypergraphs, n-ary relations, and tensors. Second, our approach integrates additional constraints in order to focus the search on clusters that are relevant for the specific application at hand. For example, if each node in a network has an annotation profile attached to it, we can identify dense clusters where the nodes share a common subprofile. The principal idea is that the user provides the datasets of interest and defines desired properties of