Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging by Zhi-Peng Li, Zhaozhen Du, De-Shuang Huang, Andrew E. Teschendorff

“…We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. …”
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Graph-aware isomorphic attention for adaptive dynamics in transformers by Markus J. Buehler

“…We present an approach for modifying transformer architectures by integrating graph-aware relational reasoning into the attention mechanism, merging concepts from graph neural networks and language modeling. …”
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Graph Neural Networks for Digital Pathology by Vincenzo DELLA MEA, Hafsa AKEBLI, Kevin ROITERO

“…The graph learning tasks can be either node-level, edge-level or graph-level. …”
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Condition monitoring of heterogeneous landslide deformation in spatio-temporal domain using advanced graph attention network by Huajin Li, Yu Zhu, Qiang Xu, Ran Tang, Chuanhao Pu, Yusen He

“…This research aims to develop an enhanced spatial-temporal monitoring system capable of capturing these complex deformation patterns. In this study, it presents a novel Graph Attention Network (GAT) framework that integrates multi-scale temporal embeddings, adaptive graph learning, and temporal self-attention mechanisms to simultaneously track localized stability variations and global deformation trends across monitoring points. …”
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