Feature selection: Key to enhance node classification with graph neural networks
Graphs help to define the relationships between entities in the data. These relationships,
represented by edges, often provide additional context information which can be utilised to …
represented by edges, often provide additional context information which can be utilised to …
[HTML][HTML] Drug discovery and development in the era of artificial intelligence: From machine learning to large language models
Abstract Drug Research and Development (R&D) is a complex and difficult process, and
current drug R&D faces the challenges of long time span, high investment, and high failure …
current drug R&D faces the challenges of long time span, high investment, and high failure …
A method for molecular design based on linear regression and integer programming
Recently a novel framework has been proposed for designing the molecular structure of
chemical compounds using both artificial neural networks (ANNs) and mixed integer linear …
chemical compounds using both artificial neural networks (ANNs) and mixed integer linear …
An inverse QSAR method based on decision tree and integer programming
Recently a novel framework has been proposed for designing the molecular structure of
chemical compounds using both artificial neural networks (ANNs) and mixed integer linear …
chemical compounds using both artificial neural networks (ANNs) and mixed integer linear …
Graph Normalizing Flows to Pre-image Free Machine Learning for Regression
Abstract In Machine Learning, data embedding is a fundamental aspect of creating
nonlinear models. However, they often lack interpretability due to the limited access to the …
nonlinear models. However, they often lack interpretability due to the limited access to the …