Computational Prediction and Prioritization of Receptor-Ligand Pairs (Computationele voorspelling en prioritisatie van receptor-ligand paren)

We have worked on the receptor-ligand pairing problem in three main studies. In our first study, using a LS-SVM classifier, we show that we are able to more aptly match members of the chemokine and tgfß families than a previously published method. Notably, we are able to achieve an increase in recall of 0.76 over the 0.44 for the matching of receptor-ligands in the tgfß family. In our subsequent study, we benchmarked several machine learning techniques, and essayed several parameters, on the receptior-ligand interaction prediction task. We found that we could reach a balanced accuracy of 0.84. In our final work, we produce a publicly available database of our results with respect to a text-based in silico prediction workflow. The resulting database, contains several key findings, particularly predictions in the GPCR family with a balanced accuracy of 0.96. The receptor-ligand prediction task is an essential one, as the challenge of predicting such pairs is an important issue in wet-labs, biotech, and pharmaceutical companies. Through several studies, we have determined the most appropriate methodology to predict the receptor-ligand pairs and have made available high-quality predictions at our ReLianceDB website (http://homes.esat.kuleuven.be/~bioiuser/ReLianceDB), a tool to aid in performing effective and targeted research.status: publishe