利用類神經網路預測膜蛋白

Prediction of Membrane Protein with Artificial Neural Network

邱名錡(ming-jou Chiu)

指導教授 ： 張培均

亞洲大學/資訊學院/生物資訊學系碩士班/碩士(2005年)

摘要

生物學中最重要的分子是嵌入細胞外層脂肪膜中的蛋白質，它們就像守門員一樣探測細胞外的重要化合物，決定哪些可進入細胞中，近年來網路上有很多預測膜蛋白的工具提供預測，且大多是針對在螺旋(helice)結構上，雖然這些工具的預測準確性佔有50-70%，但有10%的錯誤預測是因為無法正確辨識球蛋白部分，以至於使其研究發展預期過高，本研究希望利用類神經網路的方式來預測膜蛋白，資料來源取自PDB資料庫裡的593條已知的膜蛋白及500條非膜蛋白，建立資料庫並運用到目前為止可用的預測工具，來評估其準確性，再用類神經網路的方法加以整合，希望能提供一個最佳化的類神經網路來正確預測膜蛋白。

關鍵字

類神經網路 ； 膜蛋白 ； HASH(0x8ac1e10)

並列摘要

Membrane proteins are crucial for survival﹒They constitute the key components for cell–cell signal transduction﹐transport of ions or solutes across the membrane, and are crucial for recognition. Many methods predict membrane helices, but few predict membrane strands﹒The good news is that most methods for helical membrane proteins are available and have higher accuracy﹒Current prediction methods predict membrane helices for about 50%–70% accuracy, and 10%false prediction for the globular protein﹒The bad news is that developers have seriously overestimated the accuracy of their methods﹒
This research utilized neural network to predict membrane protein. The protein data were collected from PDB database. The standard data set contains 593 membrane protein sequences and 500 non-membrane protein sequences. Nine popular prediction tools on the internet were used to assess its accuracy. Then, we take the way of neural network to integrate these tools and to predict membrane protein more precise.

並列關鍵字

Membrane proteins ； neural network

參考文獻

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