MOGAD: Integrated Multi-Omics and Graph Attention for the Discovery of Alzheimer’s Disease’s Biomarkers
Abstract
1. Introduction
2. Materials and Methods
2.1. Datasets
2.1.1. Multi-Omics Data and Clinical Data
2.1.2. Other Annotation Data
- We generated an ALLRegions matrix by averaging the values of the same genes across the 6 matrices.
- We generated an ALLGeneALLRegions matrix by concatenating the above 7 matrices (after adding region-specific prefixes to their gene names) based on matching sample names.
2.1.3. Hi-C Data
2.2. Methods
2.2.1. Data Preprocessing
2.2.2. Construction of Similarity Networks
2.2.3. Pretraining of MGAT
2.2.4. Pretraining of MGAF
2.2.5. Integration of Omics Networks
2.2.6. Biomarker Discovery
2.2.7. Hyperparameter Tuning
3. Results
3.1. Experiment
3.2. The Performance of Different Models Using Multi Omics Data
3.3. Comparison of Our Model with Other Models in Terms of Training Speed and Memory
3.4. The Performance of Our Model Under Different Omics Data Types
3.5. The Performance of Our Model Using Non-Omics Data with Different Importance Score k
3.6. The Necessity of Each Module in Our Model
3.7. The Performance of Our Model Using DNA Gene Regions and CpGs
3.8. The Biomarkers Identified by Our Model
3.9. Hi-C in Research
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix A.1. Hi-C Differential Matrix Construction
Appendix A.1.1. Interaction Frequency Extraction
- (1)
- Data type: Observed values
- (2)
- Normalization: KR
- (3)
- Resolution: 1 kb
Appendix A.1.2. Differential Matrix Calculation
- : Interaction matrix from AD samples.
- : Interaction matrix from normal aging samples.
- : Stabilization matrix (Dimension: n × n, all elements = 0.001) added to prevent division by zero.
- : applied to prevent negative results.
Appendix A.1.3. Genomic Region Extension
Appendix A.2. Data Preprocessing
Appendix A.2.1. Omics Data Processing
Appendix A.2.2. Non-Omics Feature Encoding (ROSMAP)
Appendix A.3. Construction of Similarity Networks
Appendix A.3.1. Omics-Only Networks
- (1)
- Cosine Similarity Calculation:
- (2)
- Threshold-Based Sparsification:
Appendix A.3.2. Threshold Determination
Appendix A.3.3. Integrated Omics/Non-Omics Networks
- (1)
- Feature Fusion:
- (2)
- Input feature matrix:
- (3)
- Network Generation:
Appendix A.4. Pretraining of MGAT
Appendix A.4.1. Input and Architecture
Appendix A.4.2. Attention Weight Computation
Appendix A.4.3. Multi-Head Aggregation
Appendix A.4.4. Classification and Loss Function
Appendix A.4.5. Pseudo-Code for MGAT
Algorithm A1. Multi-head GAT network (MGAT) |
Input: Feature matrix for omics m
, Adjacency matrix for omics m
and Number of attention heads K. Output: Preliminary prediction results of MGAT . 1: Initialize weight matrices and attention vector 2: for each head k = 1 to K do 3: for each node j in do 4 5: end for 6: 7: end for 8: 9 10: Compute 11: Return |
Appendix A.5. Pretraining of MGAF
Appendix A.5.1. Input Encoding
Appendix A.5.2. Attention-Weighted Fusion
Appendix A.5.3. Classification and Loss
Appendix A.5.4. Notice
Appendix A.5.5. Pseudo-Code for MGAF
Algorithm A2. Multi-Graph Attention Fusion (MGAF) |
Input: Feature matrix for omics m
, Adjacency matrix for omics m
and Total number of omics datasets M. Output: Preliminary prediction results of MGAF . 1: Initialize omics-specific GATs: 2: Initialize attention weights (learnable parameters) 3: for each omics type m = 1 to M do 5: end for 6: E← 7 8 ← 9: Return |
Appendix A.6. Integration of Omics Networks
Appendix A.6.1. Fusion Mechanism
- (1)
- Input Processing:
- (2)
- Final Classification:
Appendix A.6.2. Loss Functions
Appendix A.6.3. Pseudo-Code for MGAF
Algorithm A3. Attention Fusion (AF) |
Input: Output matrix from MGAT or MGAF
, Number of input matrices T. Output: Final prediction . 1: Initialize linear layers , , …, 2: Initialize learnable attention weights , … 3: for each t = 1 to T do 4: ← 5: ← 6: end for 7: E← 8: 9: Compute MSE Loss: ← 10: Return , |
Appendix A.7. Common Ground for MGAT, MGAF and AF
Appendix A.7.1. Regularization
Appendix A.7.2. Training Strategy
Appendix A.7.3. Omics/Non-Omics Integration
Appendix A.8. Biomarker Discovery
Appendix A.8.1. How to Calculate Feature Score
Appendix A.8.2. Experiment
Appendix A.8.3. Frequency-Based miRNA Filtering
Appendix A.8.4. Biological Validation
Appendix B
Method | MOGONET | MOADLN | MoGCN | MOGAD |
---|---|---|---|---|
Multi-omics types | mRNA, Me, miRNA | mRNA, Me, miRNA | mRNA, CNV, protein | mRNA, Me, miRNA |
Clinical Data Integration | FALSE | FALSE | FALSE | TRUE |
Core components | GCN | Multi-head Attention | GCN, SNF and AE | GAT |
Hi-C Validation | FALSE | FALSE | FALSE | TRUE |
Performance (AD ACC) | 0.751 | 0.758 | None | 0.773 |
Performance (BRCA ACC) | 0.851 | 0.835 | 0.793 | 0.874 |
Modules | Pre-Learning Rate | Learning Rate | Hidden Layer | Dropout Rate | Head Number |
---|---|---|---|---|---|
MGAT (ROSMAP) | 5 × 10−3 | 5 × 10−4 | 20 | 0.5 | 3 |
MGAF (ROSMAP) | 5 × 10−3 | 5 × 10−4 | 20 | 0.5 | None |
AF (ROSMAP) | None | 1 × 10−3 | 16 | None | None |
MGAT (BRCA) | 5 × 10−3 | 5 × 10−4 | 50 | 0.5 | 3 |
MGAF (BRCA) | 5 × 10−3 | 5 × 10−4 | 200 | 0.1 | None |
AF (BRCA) | None | 1 × 10−3 | 64 | None | None |
Method | ACC | F1_Weighted | F1_Macro |
---|---|---|---|
SVM | 0.786 | 0.767 | 0.666 |
KNN | 0.644 | 0.578 | 0.513 |
RF | 0.832 | 0.817 | 0.734 |
DT | 0.780 | 0.777 | 0.734 |
NB | 0.436 | 0.323 | 0.214 |
XGBoost | 0.856 | 0.855 | 0.820 |
DNN | 0.798 | 0.802 | 0.761 |
MOGONET | 0.851 | 0.847 | 0.804 |
MOADLN | 0.835 | 0.829 | 0.793 |
MoGCN | 0.793 | 0.781 | 0.750 |
MOGAD (This study) | 0.874 | 0.874 | 0.861 |
Dataset | Metrics | MOGAD | MOGONET | MOADLN |
---|---|---|---|---|
ROSMAP | Time | 113.33 s | 72.77 s | 33.96 s |
Peak GPU memory usage | 479.27 MB | 145.96 MB | 172.64 MB | |
Peak RAM usage | 2531.36 MB | 2828.47 MB | 2529.46 MB | |
BRCA | Time | 231.96 s | 101.15 s | 63.67 s |
Peak GPU memory usage | 2377.35 MB | 378.11 MB | 458.75 MB | |
Peak RAM usage | 2529.08 MB | 2829.73 MB | 2528.39 MB |
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Method | ACC | F1-Score | AUC | MCC |
---|---|---|---|---|
SVM | 0.647 | 0.692 | 0.732 | 0.297 |
KNN | 0.590 | 0.635 | 0.627 | 0.176 |
Lasso | 0.710 | 0.729 | 0.785 | 0.417 |
Elastic-Net | 0.739 | 0.766 | 0.824 | 0.479 |
RF | 0.656 | 0.692 | 0.714 | 0.310 |
DT | 0.583 | 0.603 | 0.594 | 0.166 |
GNB | 0.515 | 0.490 | 0.498 | 0.015 |
XGBoost | 0.702 | 0.731 | 0.767 | 0.403 |
Ridge | 0.760 | 0.780 | 0.839 | 0.521 |
PLSR | 0.590 | 0.588 | 0.666 | 0.186 |
DNN | 0.674 | 0.697 | 0.749 | 0.349 |
MOGONET | 0.751 | 0.772 | 0.791 | 0.505 |
MOADLN | 0.758 | 0.786 | 0.800 | 0.524 |
MOGAD (This study) | 0.773 | 0.787 | 0.832 | 0.551 |
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Zhang, Z.; Chen, Y.; Wang, C.; Guo, M.; Cai, L.; He, J.; Liang, Y.; Wong, G.; Chen, L. MOGAD: Integrated Multi-Omics and Graph Attention for the Discovery of Alzheimer’s Disease’s Biomarkers. Informatics 2025, 12, 68. https://doi.org/10.3390/informatics12030068
Zhang Z, Chen Y, Wang C, Guo M, Cai L, He J, Liang Y, Wong G, Chen L. MOGAD: Integrated Multi-Omics and Graph Attention for the Discovery of Alzheimer’s Disease’s Biomarkers. Informatics. 2025; 12(3):68. https://doi.org/10.3390/informatics12030068
Chicago/Turabian StyleZhang, Zhizhong, Yuqi Chen, Changliang Wang, Maoni Guo, Lu Cai, Jian He, Yanchun Liang, Garry Wong, and Liang Chen. 2025. "MOGAD: Integrated Multi-Omics and Graph Attention for the Discovery of Alzheimer’s Disease’s Biomarkers" Informatics 12, no. 3: 68. https://doi.org/10.3390/informatics12030068
APA StyleZhang, Z., Chen, Y., Wang, C., Guo, M., Cai, L., He, J., Liang, Y., Wong, G., & Chen, L. (2025). MOGAD: Integrated Multi-Omics and Graph Attention for the Discovery of Alzheimer’s Disease’s Biomarkers. Informatics, 12(3), 68. https://doi.org/10.3390/informatics12030068