Pregnancy complications are a leading cause of maternal and neonatal mortality worldwide. Understanding their underlying mechanisms is hindered by dispersed evidence across thousands of studies and complex biological interactions. We present PregBase, a comprehensive knowledge base for pregnancy research comprising automated extraction, validation, and exploration. PregBase was constructed by utilising large language models (LLMs) to extract relationships from literature; 13 LLMs were benchmarked across seven prompting strategies, with ensemble shuffle prompting outperforming single-strategy alternatives. A three-tier validation pipeline combining ontology mapping, graph neural networks, and statistical analysis produced PregKG, a knowledge graph containing 64,087 associations between 13,303 biomedical entities spanning 155 semantic types and 50 vocabularies across 8 relationship types from 8420 articles. Link prediction validated PregBase's inference capability beyond extracted knowledge, recovering established clinical interventions, reconstructing canonical hormonal pathways across maternal-placental-fetal compartments, and identifying novel biomarker candidates for preterm birth, gestational diabetes, and preeclampsia, supported by genetic and expression databases. An interactive web interface (https://pregknowledgebase.com) has been created to enable further exploration via conversational queries. This work provides a scalable foundation for systematic discovery in maternal health research.
Aashish Bhandari, D. Mehta, Karin M. Verspoor et al.· Artif. Intell. Medicine· 0 citations
Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) are characterised by progressive, stage-dependent disruptions in brain connectivity. Multimodal neuroimaging data, particularly functional MRI (fMRI) and diffusion tensor imaging (DTI), provide complementary perspectives on functional and structural brain organisation. However, most existing graph-based approaches compress whole-brain networks into a single global representation, limiting their capacity to model hierarchical connectivity patterns and to deliver interpretable insights for biomarker discovery. In this work, we propose HiBrain, a hierarchical prototype-based framework for multimodal brain network analysis. HiBrain explicitly represents brain graphs at node-, graph-, and stage-level abstractions, while preserving the distinct structural and functional connectivity characteristics throughout the hierarchy. The proposed framework progressively abstracts representative local connectivity patterns into global network representations and disease-stage–specific prototypes, enabling both accurate stage-aware classification and principled interpretability. Experiments on multimodal AD and PD datasets demonstrate consistent improvements over state-of-the-art baselines in multi-stage classification tasks. Moreover, prototype-driven visualisations of connectivity difference matrices and biomarker subgraphs reveal clear and stage-specific brain network signatures, highlighting the interpretability and scientific utility of the proposed framework for neurodegenerative disease analysis. The source code is available at https://github.com/yangkf825/HiBrain.
Jing Ren, Kefan Yang, Le Linh Dan Nguyen et al.· Proceedings of the 32nd ACM...· 1 citation
Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease. Existing methods typically use GNNs and Transformers to model the full functional connectivity matrix. However, processing tens of thousands of connections introduces redundancy and noise, increases computational cost, and limits connection-level interpretability. This raises a central question: do we really need complex interaction modeling, or is identifying a small set of disease-relevant connectivity patterns sufficient? To answer this question, we propose BrainLinear, a lightweight geometry-aware framework for mining disease-discriminative connectome patterns. BrainLinear first maps each functional connectivity matrix to a shared tangent space centered at the Fr\'echet mean of the training set, capturing subject-specific deviations while respecting matrix geometry. It then scores each ROI-pair tangent direction by its classification contribution and disease--control difference, retaining Top-$K$ directions as a compact representation. Finally, a shallow multilayer perceptron performs classification on the selected representation. Experiments on ABIDE and ADNI show that BrainLinear matches or exceeds strong GNN and Transformer baselines at a fraction of their cost: it improves AUC and ACC over the best baseline for each metric by up to $3.54$ and $1.39$ percentage points, while reducing runtime and peak GPU memory by $84.0\%$ and $68.4\%$ relative to the closest baseline in AUC. The selected directions are directionally consistent with between-group displacements and organized across major functional systems, supporting connection-level interpretation.
Sijing Wu, Dongyuan Li, Miaoting Huang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
