Stage-aware Brain Graph Learning for Alzheimer's Disease
Peng, C.; Liu, M.; Meng, C.; Xue, S.; Keogh, K.; Xia, F.
Show abstract
Current machine learning-based Alzheimers disease (AD) diagnosis methods fail to explore the distinctive brain patterns across different AD stages, lacking the ability to trace the trajectory of AD progression. This limitation can lead to an oversight of the pathological mechanisms of AD and suboptimal performance in AD diagnosis. To overcome this challenge, this paper proposes a novel stage-aware brain graph learning model. Particularly, we analyze the different brain patterns of each AD stage in terms of stage-specific brain graphs. We design a Stage Feature-enhanced Graph Contrastive Learning method, named SF-GCL, utilizing specific features within each AD stage to perform graph augmentation, thereby effectively capturing differences between stages. Significantly, this study unveils the specific brain patterns corresponding to each AD stage, showing great potential in tracing the trajectory of brain degeneration. Experimental results on a real-world dataset demonstrate the superiority of our model.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- GREMI: an Explainable Multi-omics Integration Framework for Enhanced Disease Prediction and Module Identification 93%
- pathCLIP: Detection of Genes and Gene Relations from Biological Pathway Figures through Image-Text Contrastive Learning 92%
- BertNDA: a Model Based on Graph-Bert and Multi-scale Information Fusion for ncRNA-disease Association Prediction 92%
Similar papers in this journal
- ARCH: Large-scale Knowledge Graph via Aggregated Narrative Codified Health Records Analysis 91%
- A Utility-Based Machine Learning-Driven Personalized Lifestyle Recommendation for Cardiovascular Disease Prevention 90%
- An Open-Set Semi-Supervised Multi-Task Learning Framework for Context Classification in Biomedical Texts 89%
Similar papers in this journal
- Identification of functionally connected multi-omic biomarkers for Alzheimer’s Disease using modularity-constrained Lasso 91%
- Predicting Adverse Drug Effects: A Heterogeneous Graph Convolution Network with a Multi-layer Perceptron Approach 91%
- Regional medical inter-institutional cooperation in medical provider network constructed using patient claims data from Japan 91%
Similar papers in this journal
- Transformer with Convolution and Graph-Node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image 93%
- Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning 90%
- A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in resting-state functional MRI Data 90%
Similar papers in this journal
- Learning universal knowledge graph embedding for predicting biomedical pairwise interactions 93%
- iDRKAN: Interpretable miRNA-Disease Association Prediction Based on Dual-Graph Representation Learning and Kolmogorov-Arnold Network 93%
- MTGCL: Multi-Task Graph Contrastive Learning for Identifying Cancer Driver Genes from Multi-omics Data 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.