graph neural network

Somatic structural variation discovery on cancer pangenome graphs

JANUS discovers somatic structural variants in cancer genomes using a pangenome graph approach, achieving high accuracy and efficiency compared to traditional methods.

Classifying tumors from broad category to hierarchical molecular subtype: an interpretable hierarchical framework for precision oncology

This paper introduces HHRM, a hierarchical model for tumor subtype classification, achieving high accuracy in pan-cancer and breast cancer subtyping with interpretable biomarker identification.

Deep Recovery of Effective Migration Surfaces

This paper introduces dreems, a graph neural network that efficiently estimates effective migration surfaces from genetic data, achieving lower error and faster computation than existing methods.

Graph neural network approach for inferring neuronal connectivity

This paper introduces CircuitGNN, a graph neural network that infers neural connectivity from population activity, showing high accuracy and robustness across different circuit sizes and recording conditions.

FateDriver: Weakly supervised dual-view graph learning for transition-specific prioritization of candidate regulators in cellular reprogramming

FateDriver prioritizes candidate regulators for specific cellular transitions using a weakly supervised dual-view graph neural network, outperforming existing methods in recovering known regulators.

Interpreting graph neural networks reveals microbial association patterns in inflammatory bowel diseases

The paper uses graph neural networks to interpret microbial association patterns in IBD, showing that coordinated associations, not just abundance shifts, are predictive.