The inception of spatial transcriptomics has allowed improved comprehension of tissue architectures and the disentanglement of complex underlying biological, physiological, and pathological processes through their positional contexts. Recently, these contexts, and by extension the field, have seen much promise and elucidation with the application of graph learning approaches. In particular, neural operators have risen in regards to learning the mapping between infinite-dimensional function spaces. With basic to deep neural network architectures being data-driven, i.e. dependent on quality data for prediction, neural operators provide robustness by offering generalization among different resolutions despite low quality data. Graph neural operators are a variant that utilize graph networks to learn this mapping between function spaces. The aim of this research is to identify robust machine learning architectures that integrate spatial information to predict tissue types. Under this notion, we propose a study incorporating various graph neural network approaches to validate the efficacy of applying neural operators towards prediction of brain regions in mouse brain tissue samples as a proof of concept towards our purpose. We were able to achieve an F1 score of nearly 72% for the graph neural operator approach which outperformed all baseline and other graph network approaches.

翻译：空间转录组学的诞生使人们能够通过位置背景更好地理解组织结构，并厘清其中复杂的生物学、生理学及病理学过程。近年来，这些背景及其相关领域在图学习方法的应用中展现出巨大前景，并得到了深入阐释。特别是，神经算子在无限维函数空间之间的映射学习方面取得了重要进展。由于从基础到深度的神经网络架构均为数据驱动型（即依赖数据质量进行预测），神经算子通过在不同分辨率下提供泛化能力来增强鲁棒性，即使数据质量较低也能保持性能。图神经算子作为其变体，利用图网络学习函数空间之间的映射。本研究旨在识别能够整合空间信息以预测组织类型的鲁棒机器学习架构。基于此理念，我们提出了一项融合多种图神经网络方法的研究，以验证神经算子在小鼠脑组织样本脑区预测中的有效性，作为概念验证。通过图神经算子方法，我们实现了近72%的F1分数，这一结果优于所有基线及其他图网络方法。