X2Graph for Cancer Subtyping Prediction on Biological Tabular Data
This addresses the challenge of limited data in medical tabular analysis for cancer subtyping, though it appears incremental as it adapts graph techniques to a specific domain.
The paper tackled the problem of applying deep learning to small biological tabular datasets for cancer subtyping prediction by proposing X2Graph, a method that converts samples into graphs using external knowledge like gene interactions, and it achieved superior performance compared to existing methods on three datasets.
Despite the transformative impact of deep learning on text, audio, and image datasets, its dominance in tabular data, especially in the medical domain where data are often scarce, remains less clear. In this paper, we propose X2Graph, a novel deep learning method that achieves strong performance on small biological tabular datasets. X2Graph leverages external knowledge about the relationships between table columns, such as gene interactions, to convert each sample into a graph structure. This transformation enables the application of standard message passing algorithms for graph modeling. Our X2Graph method demonstrates superior performance compared to existing tree-based and deep learning methods across three cancer subtyping datasets.