| Summary: | 碩士 === 國立虎尾科技大學 === 資訊工程研究所 === 103 === Lung cancer is the leading cause of death worldwide, and non-small cell lung cancer (NSCLC) accounts for more than 85% of all lung cancer cases. However, the process of new drug development is cost-intensive and time-consuming; therefore, how to effectively search for suitable potential drugs for NSCLC has been a critical issue in biomedical research.
In the previous study, we have developed a machine learning method, based on domain-domain interactions, weighted domain frequency score and cancer linker degree data, to predict cancer proteins. In this thesis, we extended the previous study by further evaluating its performance with AUC (area under curve) measure, and applied the machine learning method to predict potential cancer genes from differentially expressed genes from microarray data. We then developed a pipeline to infer potential therapeutic drugs for disease treatment by preforming meta-analysis, and integrated the protein-protein interactions, biological pathway analysis and the cMap resources. Finally, the predicted drugs are investigated by experiments. It is expect that the drug-finding pipeline may be helpful in drug repositioning discovery for other cancer diseases.
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