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這篇文章是我在讀到 Nature Methods 在 2018 年 3 月 5 日刊登的文章「Using deep learning to model the hierarchicalstructure and function of a cell」1後,在臉書 MiTalk 社團寫下的三篇短文的整理集結。在這三篇短文中,我簡要地介紹了目前人工智慧的技術基礎「類神經網路」的概念,再將其延伸到這篇文章提及的系統生物學研究,並解釋目前類神經網路之所以被稱為「黑盒子」的原因,以及這項系統生物學研究處理黑盒子的手法。
我來用實際生活案例舉個例子好了。假設你要登一座山,目標是山頂。這座山每個地方的地型都完全不一樣。所以從 A 點上山和從不一樣的 B 點或 C 點上山的路都不盡相同。假設隨機把你放在這座山邊的某一點,要你朝著山頂為目標前進。這時候你的每一步就都會是在「那個當下」最佳的往山頂路線。所以從不同的點上山路線就有可能會差異極大,雖然最後都能到山頂就是了。
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MALDI-TOF MS 解析微生物的全細胞蛋白質分子量大小範圍在 2000-20000 Da 之間,此區間的蛋白質以核醣體蛋白等胞內負責持家的蛋白質為主4。核糖體蛋白在不同菌種都需要用到且需求量相當,所以不易受到外在培養條件影響,故質譜訊號有良好重複性與再現性,可作為菌種鑑別之依據。MALDI-TOF MS 設備的製造商已與德國菌種中心合作,將已知菌株的蛋白質指紋圖譜建立資料庫,利用相同物種指紋圖譜一致的特性,將未知樣本圖譜與已知圖譜比對,則可快速完成微生物身分鑑定。
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