  Machine learning methods can detect complex relationships between variables,
but usually do not exploit domain knowledge. This is a limitation because in
many scientific disciplines, such as systems biology, domain knowledge is
available in the form of graphs or networks, and its use can improve model
performance. We need network-based algorithms that are versatile and applicable
in many research areas. In this work, we demonstrate subnetwork detection based
on multi-modal node features using a novel Greedy Decision Forest with inherent
interpretability. The latter will be a crucial factor to retain experts and
gain their trust in such algorithms. To demonstrate a concrete application
example, we focus on bioinformatics, systems biology and particularly
biomedicine, but the presented methodology is applicable in many other domains
as well. Systems biology is a good example of a field in which statistical
data-driven machine learning enables the analysis of large amounts of
multi-modal biomedical data. This is important to reach the future goal of
precision medicine, where the complexity of patients is modeled on a system
level to best tailor medical decisions, health practices and therapies to the
individual patient. Our proposed approach can help to uncover disease-causing
network modules from multi-omics data to better understand complex diseases
such as cancer.
