论文标题
部分可观测时空混沌系统的无模型预测
Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks
论文作者
论文摘要
从基因组解码的蛋白质序列数量增加的是，有关将蛋白质序列与变压器神经网络功能联系起来的新研究途径。最近的研究表明，已知蛋白质序列的数量支持通过变压器学习有用的任务不合稳定序列表示。在本文中，我们认为学习联合序列结构表示为与功能相关的预测任务提供了更好的表示。我们提出了一个变压器神经网络，该网络同时遵循序列和三级结构。我们表明，这种联合表示仅比基于序列的表示功能更强大，并且它们在各种指标的超家族会员资格上产生更好的性能。
The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks. Recent research has shown that the number of known protein sequences supports learning useful, task-agnostic sequence representations via transformers. In this paper, we posit that learning joint sequence-structure representations yields better representations for function-related prediction tasks. We propose a transformer neural network that attends to both sequence and tertiary structure. We show that such joint representations are more powerful than sequence-based representations only, and they yield better performance on superfamily membership across various metrics.
