A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

“…Second, meaningful features were extracted using various network-based analytical methods, including proximity to disease-associated genes, as well as unbiased approaches based on propagation, topological metrics, and module detection. While these methods have been extensively applied in drug repositioning scenarios [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , their effectiveness in target repositioning remains to be better characterised. Using the extracted features, various machine learning algorithms were then trained, and the predictive power of each approach was evaluated.…”