Abstract: This article proposes a machine-learning (ML) method to accelerate atomic-level device simulation. The main idea is to utilize graph convolutional network (GCN) to predict the potential ...

Abstract: Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Circular RNAs (circRNAs) possess structural stability and tissue-specific expression ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Drug–drug interactions (DDIs) present a significant challenge in clinical practice, as they may lead to adverse reactions, diminished therapeutic efficacy, and serious risks to patient safety. However ...