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.

本文利用一种序列到序列自编码器模型以无监督的方式学习长度可变的 DNA 序列的固定维度的潜在表示，并在监督学习的拼接位点分类任务中进行了定量和定性评估。实验表明，此类表示法可用作通常相关的任务中的特征或先验，并且采用的模型归因技术获得的序列特征对分类精度有显著影响。