Drug repositioning by belief networks and ensemble method

The approach of drug repositioning, recognized as an efficient strategy to reduce costs and shorten development timelines in the pharmaceutical industry, has garnered significant interest among researchers. With vast datasets rich in valuable insights, machine learning models are not only widely applied but also continuously enhanced to improve accuracy and reliability. However, the process still faces notable challenges due to the complexity of relationships between drugs, diseases, and target proteins within biological networks. This paper focuses on optimizing the predictive performance of drug-disease relationship models by identifying potential connections through a belief network framework. The approach enables selection of the most effective graph path for each drug-disease pair, thus improving prediction accuracy by prioritizing critical biological interactions. Leveraging an ensemble of four classification methods along with a voting mechanism, the belief network ensures high adaptability and reliability across various datasets and disease types. Experimental results demonstrate that proposed analytical framework achieves not only superior performance but also high reliability in identifying potential drug-disease associations. Case studies within the paper further validate the model's capability to discover promising drug candidates for challenging diseases, underscoring the practical significance.