Porter: a new, accurate server for protein secondary structure prediction
Porter: a new, accurate server for protein secondary structure prediction
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DOI：
10.1093/bioinformatics/bti203
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发表时间：
2005-04-15
期刊：
影响因子：
5.8
通讯作者：
McLysaght, A
中科院分区：
文献类型：
--
作者：
Pollastri, G;McLysaght, A
PORTER是一个新的蛋白质二级结构预测系统，分为三类。Porter依赖于具有快捷连接的双向递归神经网络、从多个序列比对获得的输入轮廓的准确编码、递归神经网络的第二级过滤、结合远程信息和大规模预测器集成。通过对大量蛋白质进行严格的5倍交叉验证，Porter的准确率超过79%，远远高于最先进的SSpro服务器的副本，比迄今发布的任何系统都要好。
Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date.