Transmembrane helix prediction using feed-forward neural network

M. Mottalib, Md Safiur Rahman Mahdi, A. Haque, S.M. Al Mamun, H. A. A. Mamun
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Abstract

Neural network is one of the successful methods for protein secondary structure prediction. Day to day this technology is modified, improved, even other methods also combined with it to get better result. In this paper we trained feed-forward neural network with trans-membrane protein for helix prediction. Using Java Object Oriented Neural Engine (JOONE) our achieved accuracy is 71%. This paper is expected to benefit researchers in proteomics by presenting a summary of developments of neural network in this area.
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基于前馈神经网络的跨膜螺旋预测
神经网络是蛋白质二级结构预测的成功方法之一。这项技术每天都在改进,改进,甚至其他方法也与之相结合,以获得更好的效果。本文利用跨膜蛋白训练前馈神经网络进行螺旋预测。使用Java面向对象神经引擎(JOONE),我们的准确率达到71%。本文对神经网络在蛋白质组学研究中的发展进行了综述,以期对蛋白质组学研究人员有所裨益。
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