Think about someone you’d call a friend. What’s it like when you’re with them? Do you feel connected? Like the two of you are in sync? In today’s story, we’ll meet two friends who have always been in ...
Abstract: Recently, many post hoc graph neural network (GNN) explanation methods have been explored to uncover GNNs' predictive behaviors by analyzing the embeddings produced by the GNN models.
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Lists and Animated Graphs in webVpython (Glowscript)
Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...
Adapting to the stream: An instance-attention GNN method for irregular multivariate time series data
Framework of DynIMTS. The model is a recurrent structure based on a spatial-temporal encoder and consists of three main components: embedding learning, spatial-temporal learning, and graph learning.
ABSTRACT: Drug repositioning aims to identify new therapeutic applications for existing drugs offering a faster and more cost-effective alternative to traditional drug discovery. Since approved drugs ...
This project implements a drug-disease association prediction model using Graph Convolutional Networks (GCN) with advanced data augmentation techniques. The model predicts novel drug-disease ...
Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United ...
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