ArXiv Preprint
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.
Junaid Ahmed, Alhassan S. Yasin
2023-02-01
