Patient similarity networks (PSNs) are a widely used tool in biomedical data analysis. The most powerful feature of PSNs (and all networks in general) is the ability to visualize relatively complex data in a way that people can understand without expertise in statistics and machine learning. This aspect of data analysis is particularly important in biomedical data analysis, where data analysts, clinicians, and people from laboratories work in collaborative teams. However, working with PSNs requires steps that are usually solved in different systems or using programming libraries, e.g., in R or Python. This paper presents a tool whose design results from several years of experience with PSNs in biomedical data analysis. The tool focuses on the essential tasks of transforming vector data into PSNs and exploring them in detail, as well as on a high level of interactivity, e.g. allowing the formulation of preliminary hypotheses. We demonstrate the practical use of the tool on a well-known biological dataset.