标题： 无监督的DNA序列表示学习 显示英文标题

标题： Unsupervised Representation Learning of DNA Sequences

摘要： 最近，几种深度学习模型已被用于基于DNA序列的分类任务。 通常，此类任务需要输入长且长度可变的DNA序列。 在本工作中，我们使用序列到序列的自编码器模型，在无监督方式下为长且长度可变的DNA序列学习一个固定维度的潜在表示。 我们对监督任务中的剪接位点分类进行了定量和定性评估。 定量评估是在两种不同的设置下进行的。 我们的实验表明，这些表示可以作为特征或先验知识用于密切相关的任务，如剪接位点分类。 此外，在我们的定性分析中，我们使用模型归因技术集成梯度来推断影响分类准确性的显著序列特征。 我们展示的识别出的剪接特征与现有知识非常相似。 显示英文摘要

摘要： Recently several deep learning models have been used for DNA sequence based classification tasks. Often such tasks require long and variable length DNA sequences in the input. In this work, we use a sequence-to-sequence autoencoder model to learn a latent representation of a fixed dimension for long and variable length DNA sequences in an unsupervised manner. We evaluate both quantitatively and qualitatively the learned latent representation for a supervised task of splice site classification. The quantitative evaluation is done under two different settings. Our experiments show that these representations can be used as features or priors in closely related tasks such as splice site classification. Further, in our qualitative analysis, we use a model attribution technique Integrated Gradients to infer significant sequence signatures influencing the classification accuracy. We show the identified splice signatures resemble well with the existing knowledge.