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  • 學位論文

人工智能在藥物基因和基因體蛋白的應用前景

The Perspective of Application of Artificial Intelligence in Pharmacogenomics and Pharmacoproteomics

指導教授 : 鄭慧文
共同指導教授 : 張偉嶠(Wei-Chiao Chang)

摘要


透視人工智慧在藥物基因體學的未來:精準醫療的臨床運用之旅 中文摘要 藥物基因體學/藥物蛋白質體學在建立精準醫學的臨床運用上扮演了重要的角色,也協助醫療服務由傳統的治療方式逐漸轉向個人化的治療方式。新興的人工智慧技術(一種使用高速電腦運算演算法科技)帶來了極大的承諾,可以加速日常的醫療品質進步(生理活動)。二種新興科技的?流與融入,未來的發展趨勢、對社會的衝擊及挑戰,是本論文探討的議題。文獻資料來源包括PubMed/Medline、Google 、政府主管機關的官網(臨床試驗部分及政策)、全球性的商業顧問公司的官網(未來趨勢分析)。文獻搜尋結果顯示人工智慧在藥物基因體/蛋白質體研究非常有限,但在精準醫療上尚有些許進展。大部分的運用都是仍在研究階段,著重於資料處理及與藥物研發或安全用藥的預測上。基於隠私權的考量及經費缺乏的原因,臨床上的使用仍十分緩慢。科學、技術與社會(STS)分析指出隱私權、近日媒體的負面消息及法律規範問題均是大衆所關注的議題,在科技面及人性面中如何取得平衡至為重要。人工智慧勢必會在未來精準醫療的臨床使用上有所貢獻。

並列摘要


The artificial intelligence focuses on non-human technological objects, while pharmacogenomics and pharmacoproteomics deal with biological (life) processes. Artificial intelligence, which is the development of computer systems which have cognitive functions, promises to be a solution in facilitating clinical practice of precision medicine. The key fields of precision medicine which AI can be applied in medicine include pharmacogenomics and pharmacoproteomics. The use of artificial intelligence in healthcare and medicine has been widely reported. However, in pharmacogenomics and pharmacoproteomics, information is lacking. Therefore, this review will address following areas of AI in pharmacogenomics and pharmacoproteomics: applications, major achievements, role in shaping precision medicine, challenges hampering growth, future direction, and social issues and impact: ethical, fears and legal. This will be addressed by surveying literature from all National Center for Biotechnology Information (NCBI) databases (PubMed), Google Scholar, government organizations websites, conference proceedings, artificial intelligence company’s websites, and open Google search engine.

參考文獻


1. Apostolatos E. 2015 25 April. Precision Medicine: Revamping the “One-Size-Fits-All” Approach to Healthcare. In Havard Science Review. . Accessed 2018 25 April.
2. Hodson R. Precision medicine. Nature 2016;537:S49
3. Karczewski KJ, Daneshjou R, Altman RB. Chapter 7: Pharmacogenomics. PLOS Computational Biology 2012;8:e1002817
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