The Defense Advanced Research Projects Agency awarded Professor Jie Gu and co-PIs from the University of Minnesota and Duke ...

The next generation of engineers and scientists is moving beyond conventional computing to master interdisciplinary fields.

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph neural networks (GNNs) have emerged as a powerful tool in predicting molecular ...

Abstract: Large language models (LLMs) have significantly advanced computational biology by enabling the integration of molecular, protein, and natural language data to accelerate drug discovery.

Abstract: Graph Neural Networks (GNNs) have emerged as a powerful framework for modeling complex interconnected systems, hence making them particularly well-suited to address the growing challenges of ...

Background: Molecular interactions are central to numerous challenges in chemistry and the life sciences. Whether in solute–solvent dissolution, adverse drug–drug interactions, or protein complex ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...

This step is optional if you already have a conda environment with the required dependencies.