Search Results for author:
Found 7 papers, 3 papers with code
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update
Existing methods primarily capture only local or global structures of each node within a snapshot using message-passing and random walk-based methods.
CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings
Electronic health records (EHRs) provide a comprehensive source of longitudinal patient data, encompassing structured modalities such as laboratory results, imaging data, and vital signs, and unstructured clinical notes.
Longitudinal Ensemble Integration for sequential classification with multimodal data
In this study, we developed multiple configurations of a novel multimodal and longitudinal learning framework, Longitudinal Ensemble Integration (LEI), for sequential classification.
IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration
To shed light on personalized medicine and unravel previously unknown characteristics within integrative analysis of multi-omics data, we introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications, named Integrative Graph Convolutional Networks (IGCN).
TA-RNN: an Attention-based Time-aware Recurrent Neural Network Architecture for Electronic Health Records
For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit.
Current and future directions in network biology
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales.
GRAF: Graph Attention-aware Fusion Networks
Finally, GRAF utilizes Graph Convolutional Network (GCN) on the fused network and incorporates node features on graph-structured data for a node classification or a similar downstream task.
