【摘 要】
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The prediction of residue solvent accessibility(RSA)can provide more information for analyzing protein structures and functions.Many computing methods have been proposed to predict it for better perfo
【出 处】
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第二届中国计算机学会生物信息学会议
论文部分内容阅读
The prediction of residue solvent accessibility(RSA)can provide more information for analyzing protein structures and functions.Many computing methods have been proposed to predict it for better performance of prediction is more useful for protein study.In this work,we present a deep learning method to predict the solvent accessibility which is based on stacked deep bidirectional recurrent network(SDBRNN)applied to the sequence profiles.By this method,the continuous relative solvent accessible area prediction as well as two-state discrete prediction can be obtained.The Bidirectional Long Short-term Memory(BLSTM)network,a typical bidirectional recurrent network is first adopted to predict RSA.In order to obtain more long-ranged sequence information,merging operator is proposed when bidirectional information from hidden nodes is merged for outputting.
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