Application of a single-cell-RNA-based biological-inspired graph neural network in diagnosis of primary liver tumors.

ABSTRACT: Single-cell technology depicts integrated tumor profiles including both tumor cells and tumor microenvironments, which theoretically enables more robust diagnosis than traditional diagnostic standards based on only pathology. However, the inherent challenges of single-cell RNA sequencing (scRNA-seq) data, such as high dimensionality, low signal-to-noise ratio (SNR), sparse and non-Euclidean nature, pose significant obstacles for traditional diagnostic approaches. The diagnostic value of single-cell technology has been largely unexplored despite the potential advantages. Here, we present a graph neural network-based framework tailored for molecular diagnosis of primary liver tumors using scRNA-seq data. Our approach capitalizes on the biological plausibility inherent in the intercellular co

SUBMITTER: Zhang DH 

PROVIDER: S-EPMC11445937 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature