  Biomarker identification is critical for precise disease diagnosis and
understanding disease pathogenesis in omics data analysis, like using fold
change and regression analysis. Graph neural networks (GNNs) have been the
dominant deep learning model for analyzing graph-structured data. However, we
found two major limitations of existing GNNs in omics data analysis, i.e.,
limited-prediction (diagnosis) accuracy and limited-reproducible biomarker
identification capacity across multiple datasets. The root of the challenges is
the unique graph structure of biological signaling pathways, which consists of
a large number of targets and intensive and complex signaling interactions
among these targets. To resolve these two challenges, in this study, we
presented a novel GNN model architecture, named PathFormer, which
systematically integrate signaling network, priori knowledge and omics data to
rank biomarkers and predict disease diagnosis. In the comparison results,
PathFormer outperformed existing GNN models significantly in terms of highly
accurate prediction capability ( 30% accuracy improvement in disease diagnosis
compared with existing GNN models) and high reproducibility of biomarker
ranking across different datasets. The improvement was confirmed using two
independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The
PathFormer model can be directly applied to other omics data analysis studies.
