| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 106 === The journey of a drug, from being selected in the laboratory to finally be sold on the market, is tedious, money-consuming and full of risks. It is an urgent need to shorten the process of drug discovery and development. Either accelerating the initial phase – drug discovery or repurposing existing drugs for new indications could be beneficial to achieve the goal. In this study, we have developed an analysis pipeline for predicting potential targets of drugs based on only perturbational profiles in L1000 data. Through analyzing the associations between compounds and short hairpin RNAs (shRNAs), the potential drug-target interactions could be inferred. The performance of the prediction results was evaluated by a known dataset extracted from Drug Repurposing Hub, and an average area under the receiver operating characteristic curve (AUC) of 0.71 has been achieved. Finally, we further applied our approach to explore opportunities for drug repurposing in cancer and inflammatory diseases through functional analysis. Several putative anti-cancer drugs and anti-inflammatory drugs revealed from the prediction are supported by preclinical or clinical studies, which demonstrated the efficacy of the proposed approach.
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