Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly
cause morbidity and mortality. Thus, the identification of potential DDIs is essential for …

Drug discovery and development is a complex and costly process. Machine learning
approaches are being investigated to help improve the effectiveness and speed of multiple …

Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …

Introduction Knowledge graphs have proven to be promising systems of information storage
and retrieval. Due to the recent explosion of heterogeneous multimodal data sources …

Drug–drug interactions (DDIs) prediction is a challenging task in drug development and
clinical application. Due to the extremely large complete set of all possible DDIs, computer …

Model editing aims to precisely alter the behaviors of large language models (LLMs) in
relation to specific knowledge, while leaving unrelated knowledge intact. This approach has …