Improving the prediction of potential drugs for non-small cell lung cancer by using deep learning approach and gene mutation data

• Main Authors: PANISA DECHWECHPRASIT, 朱俐紅
• Other Authors: HUANG, CHIEN-HUNG
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/eu22vt

碩士 === 國立虎尾科技大學 === 資訊工程系碩士班 === 107 === Lung Cancer is one of the diseases that leading causes of death worldwide and it is a hot research topic. Drug repositioning is an effective approach for identifying and developing potential new therapeutic opportunities for existing drugs in a different disease. Drug repositioning provides the best tradeoff between drug discovery and risk has yielded some successes. On the other hand, mining the hidden rules and deriving useful information by deep learning technique has been a trend. In this thesis, we propose to make use of the drug repositioning approach to identify potential drugs for non-small cell lung cancer by integrating deep learning approach and gene mutation data. This work integrates two pieces of information - cancer protein prediction and gene mutation data – to develop a novel pipeline of drug repositioning to analyze lung cancer datasets from protein-protein interaction, domain database, GO ID, TCGA, and cancer-associate genes. High confident cancer protein prediction is obtained by well-training a deep learning approach using five molecular features. The proposed approach can achieve an F1 ratio of 82% for lung cancer protein prediction. In this study, the sets of key genes are first derived from deep learning approach and gene mutation data, and then they are used to infer potential-repositioning drugs from the cMap database. The efficiency of these drugs is supported from the literature review, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy (53%) than some competing methods, which demonstrates the efficiency of the proposed drug repositioning pipeline.