We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

中文翻译：

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深度学习可改善CRISPR-Cpf1指导RNA活性的预测。

我们提出了两种算法来预测AsCpf1指导RNA的活性。在基于卷积神经网络的深度学习框架中，使用了15,000个目标序列的Indel频率来训练Seq-deepCpf1。然后，我们合并了染色质可访问性信息，以创建可用于此类信息的细胞系的性能更好的DeepCpf1算法，并表明这两种算法在我们自己和已发布的数据集上均优于以前的机器学习算法。