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Drug Target Prediction for Colorectal Cancer by Combining Ontology and Network Approaches

Drug discovery is a time-consuming and expensive process, especially for complex diseases. In the last decade, targetbased methods for drug discovery have become more common and effective comparing to traditional observation-based drug discovery. Recently, computational approaches for target prediction and drug repurposing have become more common and effective compared to traditional observation-based drug discovery. However, since the data about underlying molecular mechanisms of drugs distribute among different knowledge domains and different databases, it is very challenging to design effective strategies to discover novel drug targets and propose successful drug repurposing. To alleviate this problem, we propose a computational framework to integrate complex relationship among different types of data and infer the potential drug targets by using the semantic web technology, and to improve performance through network neighborhood effect modeling. In this study, we utilize the colorectal cancer (CRC) as a proof-of-concept use case to evaluate the approach.