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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture
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Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture

机译：使用CNN和LSTM堆叠在SEQ2SEQ架构中的MICRNA序列预测

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摘要

CNN and LSTM have proven their ability in feature extraction and natural language processing, respectively. So, we tried to use their ability to process the language of RNAs, i.e., predicting sequence of microRNAs using the sequence of mRNA. The idea is to extract the features from sequence of mRNA using CNN and use LSTM network for prediction of miRNA. The model has learned the basic features such as seed match at first 2-8 nucleotides starting at the 50 end and counting toward the 30 end. Also, it was able to predict G-U wobble base pair in seed region. While validating on experimentally validated data, the model was able to predict on average 72 percent of miRNAs for specific mRNA and shows highest positive expression fold change of predicted targets on a microarray data generated using anti 25 miRNAs compare to other predicted tools. Codes are available at https://github.com/rajkumar1501/sequence-predictionusing-CNN-and-LSTMs
机译：CNN和LSTM分别证明了它们在特征提取和自然语言处理中的能力。因此，我们试图利用它们处理RNA的语言的能力，即使用mRNA的序列预测微大血清瘤的序列。该思想是使用CNN提取MRNA序列的特征，并使用LSTM网络进行miRNA预测。该模型已经了解到在前2-8个核苷酸处的基本特征，例如在50末开始的核苷酸，并朝向30末端计数。此外，它能够在种子区域预测G-U摆动碱基对。在验证在实验验证的数据上，该模型能够预测特定mRNA的平均72％的miRNA，并且显示使用反25 miRNA与其他预测工具相比生成的微阵列数据上预测目标的最高正表达式变化。代码在https://github.com/rajkumar1501/sequence-predictionusing-cnn-and-lstms

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