Pre-trained models have been successful in many protein engineering tasks.
Most notably, sequence-based models have achieved state-of-the-art performance
on protein fitness prediction while structure-based models have been used
experimentally to develop proteins with enhanced functions. However, there is a
research gap in comparing structure- and sequence-based methods for predicting
protein variants that are better than the wildtype protein. This paper aims to
address this gap by conducting a comparative study between the abilities of
equivariant graph neural networks (EGNNs) and sequence-based approaches to
identify promising amino-acid mutations. The results show that our proposed
structural approach achieves a competitive performance to sequence-based
methods while being trained on significantly fewer molecules. Additionally, we
find that combining assay labelled data with structure pre-trained models
yields similar trends as with sequence pre-trained models.

通过使用改良版图神经网络和序列为基础的方法进行比较，本文旨在填补预测最优蛋白变体的结构和基序方法之间的研究缺口，结果显示出使用这种新方法在训练更少的分子数量时实现了接近基序方法的竞争性性能，此外，将结构预训练模型与测定的标记数据相结合，可以产生与序列预训练模型类似的趋势。

利用等变图神经网络预测蛋白质变异体

Predicting protein variants with equivariant graph neural networks

In recent years, the rise of deep learning and automation requirements in the
software industry has elevated Intelligent Software Engineering to new heights.
The number of approaches and applications in code understanding is growing,
with deep learning techniques being used in many of them to better capture the
information in code data. In this survey, we present a comprehensive overview
of the structures formed from code data. We categorize the models for
understanding code in recent years into two groups: sequence-based and
graph-based models, further make a summary and comparison of them. We also
introduce metrics, datasets and the downstream tasks. Finally, we make some
suggestions for future research in structural code understanding field.

本综述介绍了编码数据的结构的两种模型，即基于序列和基于图形的模型的概述和比较，并介绍了度量、数据集和下游任务等相关知识。最后，我们对结构代码理解领域的未来研究提出了一些建议。

结构化代码理解的深度学习模型综述

A Survey of Deep Learning Models for Structural Code Understanding

Document-level relation extraction (DocRE) aims to determine the relation
between two entities from a document of multiple sentences. Recent studies
typically represent the entire document by sequence- or graph-based models to
predict the relations of all entity pairs. However, we find that such a model
is not robust and exhibits bizarre behaviors: it predicts correctly when an
entire test document is fed as input, but errs when non-evidence sentences are
removed. To this end, we propose a Sentence Importance Estimation and Focusing
(SIEF) framework for DocRE, where we design a sentence importance score and a
sentence focusing loss, encouraging DocRE models to focus on evidence
sentences. Experimental results on two domains show that our SIEF not only
improves overall performance, but also makes DocRE models more robust.
Moreover, SIEF is a general framework, shown to be effective when combined with
a variety of base DocRE models.

提出了一种用于 DocRE 的 SIEF 框架，通过设计句子重要性评分和句子聚焦损失来鼓励 DocRE 模型专注于证据句子，以提高整体性能和鲁棒性。