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Found 6 papers, 3 papers with code
CT-Agent: Clinical Trial Multi-Agent with Large Language Model-based Reasoning
Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due to limited access to external knowledge.
TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction
The clinical trial process, also known as drug development, is an indispensable step toward the development of new treatments.
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development.
Ensemble Learning for Graph Neural Networks
Graph Neural Networks (GNNs) have shown success in various fields for learning from graph-structured data.
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on ogbl-ddi
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
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on ogbl-biokg
Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI
Meanwhile, the brain disease related regions can be highlighted by the attention mechanism.
