In many areas of science and technology data describing a phenomenon or a system of interest and its individual components can be obtained from different types of instruments, using different acquisition techniques and experimental setups. Furthermore, many systems can be characterized by different data sets obtained by observing the system from different perspectives, under different conditions, or at different points in time. For example, in biology and biomedicine, rapid technological advances have led to the production of such heterogeneous and multimodal data, and enabled construction of complex networks with various types of interactions between diverse biological entities. Conventional network data analysis methods were shown to be limited in dealing with such multi-relational network data, as they are designed
specifically for analysis of single-relational networks. Therefore, in this Thesis, we propose novel integrative methods that can collectively mine multiple types of biological networks and create more accurate integrative
models capable of producing more holistic, systems-level biological insights. The proposed methods are based on Non-negative Matrix Factorization, a dimensionality
reduction technique, that is further extended for learning and modelling dependencies between large-scale heterogeneous and multimodal network data. Our methods have a wide range of applications of which we focus on those from systems biology and biomedicine, including protein function prediction, multiple network alignment, extraction of composite functional modules from multimodal biological networks, and problems in precision medicine, including patient strati cation, drug repurposing and cancer driver gene prediction. In each of these applications we show that our methods outperform existing methods, provide biological insights and generate novel hypothesis that could guide future research and biological experiments.