Machine learning algorithms for predicting drugs–tissues relationships

摘要: Abstract The prediction of drug candidates for given tissues organisms based on expression data is a critical biological problem. By correctly predicting tissues, biologists can (1) avoid an experimental process high-throughput screening that requires excessive time and costly equipment (2) accelerate the discovery by automatically assigning candidates. Although high throughput therapeutic compounds lead to generation data, candidate drugs such remains rigorous task. Hence, design high-performance machine learning (ML) algorithms crucial analysts who work with clinicians. Clinicians incorporate advanced ML tools into expert intelligent systems improve accurately identifying transfer approaches are necessary performance several tasks involved in presented this paper. performances compared setting employing evaluation measures real obtained from experiments conducted rats identify results show proposed outperform baseline terms statistical significance.