Abstract: Recently, graph neural networks (GNNs) have been applied to various circuit applications, where circuit topology is leveraged in the learning of the models. However, the aggregation of GNN ...

Word Embedding (Python) is a technique to convert words into a vector representation. Computers cannot directly understand words/text as they only deal with numbers. So we need to convert words into ...

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 ...

Multiplication in Python may seem simple at first—just use the * operator—but it actually covers far more than just numbers. You can use * to multiply integers and floats, repeat strings and lists, or ...

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 ...

Covid-19 broke the charts. Decades from now, the pandemic will be visible in the historical data of nearly anything measurable today: an unmistakable spike, dip or jolt that officially began for ...

The TensorFlow GNN library makes it easy to build Graph Neural Networks, that is, neural networks on graph data (nodes and edges with arbitrary features). It provides TensorFlow code for building GNN ...