Semantic Link Association Prediction for Drug Target Prediction

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The rapidly increasing amount of public data in chemistry and biology allows investigation of complex mechanisms of action of drugs by the systematic integration and analysis of the heterogeneous data into a semantic linked network. Some hidden relations (e.g., drug target relation) in the rich and well organized network could be inferred by statistical models. Our work particularly investigated drug target relation using the data annotated and integrated in our previous work (Chem2Bio2RDF,Chem2Bio2OWL). In this system (SLAP), you are able to do 1) one drug target pair association prediction and network exploration 2) drug target prediction against hundreds of proteins 3) target ligands identification and prediction 4) batch drug target pairs prediction 5) similar drugs identification based on biological functions 6) similar target identification based on ligand information 7) drug target prediction using other predictive models. This system is developed by Bin Chen under the supervision of David Wild and Ying Ding. Qian Zhu joined the development of web site. You are most welcome to send us your feedback