| Title: | BIGPN: Biologically informed graph propagational network for plasma proteomic profiling of neurodegenerative biomarkers |
| Journal: | Artificial Intelligence in Medicine |
| Published: | 15 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40845541/ |
| DOI: | https://doi.org/10.1016/j.artmed.2025.103241 |
Publication 19735
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Abstract
Neurodegenerative diseases involve progressive neuronal dysfunction, requiring identification of specific pathological features for accurate diagnosis. Although cerebrospinal fluid analysis and neuroimaging are commonly employed, their invasiveness and high-cost limit widespread clinical use. In contrast, blood-based biomarkers offer a non-invasive, cost-effective, and accessible alternative. Recent advances in plasma proteomics combined with machine learning (ML) have further improved diagnostic accuracy; however, the integration of underlying biological information remains largely overlooked. Notably, many ML-based plasma proteomic profiling approaches overlook protein-protein interactions (PPI) and the hierarchical structure of molecular pathways. To address these limitations, we propose Biologically Informed Graph Propagational Network (BIGPN), a novel ML model for plasma proteomic profiling of neurodegenerative biomarkers. BIGPN employs graph neural network-based architecture to harness a PPI network and propagates independent effects of proteins through the PPI network, capturing higher-order interactions with global awareness of PPIs. BIGPN then applies a multi-level pathway structure to extract biologically meaningful feature representations, ensuring that the model reflects structured biological mechanisms, and it provides clear explainability of the pathway structure in the context of importance through probabilistically represented parameters. Experimental validation on the UK Biobank dataset demonstrated the superior performance of BIGPN in neurodegenerative risk prediction, outperforming comparison methods. Furthermore, the explainability of BIGPN facilitated detailed analyses of the discriminative significance of synergistic effects, the predictive importance of proteins, and the longitudinal changes in biomarker profiles, reinforcing its clinical relevance. Overall, BIGPN's integration of PPIs and pathway structure addresses critical gaps in ML-based plasma proteomic profiling, offering a powerful approach for improved neurodegenerative disease diagnosis.</p>
8 Keywords
- Biomarkers
- Blood Proteins
- Humans
- Machine Learning
- Neural Networks, Computer
- Neurodegenerative Diseases
- Protein Interaction Maps
- Proteomics
6 Authors
- Sunghong Park
- Dong-Gi Lee
- Juhyeon Kim
- Masaud Shah
- Hyunjung Shin
- Hyun Goo Woo
1 Application
| Application ID | Title |
|---|---|
| 79011 | Development of artificial intelligence model for dementia precision medicine and diversification of diagnosis |
Enabling scientific discoveries that improve human health