Digital Therapeutics Product Repositioning via Multiplex Link Prediction Based on Genomic, Chemical, and Technological Information.

Digital therapeutics (DTx) companies are making efforts to expand their consumer base in the growing market. To this end, many of them reposition their existing products to identify new target diseases. In this study, we provide a link prediction framework for DTx product repositioning on the basis of a multiplex disorder network by integrating multiple data sources, such as associated genes between disorders, shared drugs, and patents of treatment protocols. To capture the disorders' latent features, both random-walk-based and deep-learning-based graph embedding methods are applied to transform the graph structure into vectors. Consequently, new indications are suggested for DTx products based on the cosine similarity between original and candidate disorders. Our framework was applied to five psychiatric DTx products to determine new target disorders that have the highest treatment potential for each product. Therefore, the study results are expected to assist DTx firms in entering the novel target market with low risk within a short period. Moreover, the applicability of DTx products to a wider variety of disorders may increase access to overall patient groups and gradually improve public health.