Spatial Perception for Structured and Unstructured Data In topological Data Analysis

Recent years have witnessed the accumulation of vast amounts of data and information. It is difficult to capture the characteristics of these data spatially or visualize them robustly and stably with respect to data updates and increases using conventional methods. The purpose of this study is to systematically visualize the relationships among drugs using diverse information. While studies have conducted visualization research using structured data, such as chemical descriptors, research has not yet been performed from comprehensive viewpoints using unstructured data on efficacy, adverse events, and other phenomena. Therefore, we use a topological data analysis mapper and a spatial perception method to obtain and visualize data based on the integrated principal component score of quantitative and qualitative data. Consequently, a network composed of characteristic clusters according to drug class was shown. Findings show that heterogeneous compounds in the cluster may indicate the potential for drug repositioning. Our proposed method is an effective means of obtaining new knowledge of pharmaceuticals.