Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Despite the fact that deep learning has achieved remarkable success in various domains
over the past decade, its application in molecular informatics and drug discovery is still …

Understanding protein function and develo** molecular therapies require deciphering the
cell types in which proteins act as well as the interactions between proteins. However …

Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …

Abstract Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.
GNNs combine node feature information with the graph structure by recursively passing …

This book is a foundational guide to graph representation learning, including state-of-the art
advances, and introduces the highly successful graph neural network (GNN) formalism …

Deep learning methods for graphs achieve remarkable performance on many tasks.
However, despite the proliferation of such methods and their success, recent findings …

Biological entities are involved in intricate and complex interactions, in which uncovering the
biological information from the network concepts are of great significance. Benefiting from …

Molecular interaction networks are powerful resources for molecular discovery. They are
increasingly used with machine learning methods to predict biologically meaningful …

Phenotype robustness to environmental fluctuations is a common biological phenomenon.
Although most phenotypes involve multiple proteins that interact with each other, the basic …