Recently developed deep learning techniques have significantly improved the
accuracy of various speech and image recognition systems. In this paper we show
how to adapt some of these techniques to create a novel chained convolutional
architecture with next-step conditioning for improving performance on protein
sequence prediction problems. We explore its value by demonstrating its ability
to improve performance on eight-class secondary structure prediction. We first
establish a state-of-the-art baseline by adapting recent advances in
convolutional neural networks which were developed for vision tasks. This model
achieves 70.0% per amino acid accuracy on the CB513 benchmark dataset without
use of standard performance-boosting techniques such as ensembling or multitask
learning. We then improve upon this state-of-the-art result using a novel
chained prediction approach which frames the secondary structure prediction as
a next-step prediction problem. This sequential model achieves 70.3% Q8
accuracy on CB513 with a single model; an ensemble of these models produces
71.4% Q8 accuracy on the same test set, improving upon the previous overall
state of the art for the eight-class secondary structure problem. Our models
are implemented using TensorFlow, an open-source machine learning software
library available at TensorFlow.org; we aim to release the code for these
experiments as part of the TensorFlow repository.

本文研究了如何应用深度学习技术，创建一种新的卷积神经网络结构，用于蛋白质序列的次级结构预测，并在 CB513 基准数据集上达到了 71.4％Q8 准确率。