In this study, a computational method was explored for drug repositioning using both graph-based representation for Graph Neural Networks (GNN) and feature-based representations for Machine Learning ...

Specifically, the proposed scGANSL method first constructs two views using highly variable genes (HVGs) screening and principal component analysis (PCA). They are then individually fed into a ...

Background: Identifying the associations between transfer RNA (tRNA) and diseases is critical for disease diagnosis and treatment. Computational methods offer an efficient approach for exploring these ...

License The license for Industrial Machinery Anomaly Detection using an Autoencoder is available in the license.txt file in this GitHub repository.

Tensorflow 2.0 implementation of "Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning" in ICCV2019 ...

As the popularity of cryptocurrencies grows, the threat of phishing scams on trading networks is growing. Detecting unusual transactions within the complex structure of these transaction graphs and ...

However, there has been little research on knowledge graph construction based on power transmission and transformation projects. The goal is to detect defects in power transmission and transformation ...

Unsupervised anomaly detection (AD) methods, either reconstruction based or prediction based, determine anomalies based on residuals. Occasional mutations in a single variable can cause the residuals ...

Anomaly detection is the process of finding items in a dataset that are different in some way from the majority of the items. For example, you could examine a dataset of credit card transactions to ...

In this paper, we have placed IRC safety at the heart of anomaly detection for the first time by constructing a graph neural network autoencoder that employs Energy-Weighted Message-Passing, which ...