An AI tool has made a step forward in translating the language proteins use to dictate whether they form sticky clumps ...

However, existing Heterogeneous Graph Neural Networks (HGNN) primarily focus on HGs with single ... Heterogeneous Graph model (SF-BMHG), aimed at solving the challenges of multiple edge relationships ...

Chainlink's integration of Pi Network into its Data Streams expands access to real ... The approach combines off-chain aggregation with on-chain verification for scalable, fast, and reliable ...

Here, we showcase how our graph neural network (GNN)-based implicit solvent (GNNIS ... continuum-based implicit solvent models and supports our strategy to train multiple solvents in a joint manner.

Multiple chains have been affected, resulting in big-name brands like Wetherspoons and Frankie & Benny's closing branches. Some chains have not survived, Byron Burger fell into administration last ...

As the pharmaceutical industry continues to evolve, there’s a growing recognition of the importance of multi-enterprise supply chain networks. They connect multiple supply chain partners, including ...

To efficiently solve the problem, we propose a novel graph neural network (GNN)-based framework. Specifically, we first model the interactions among users and network entities using a heterogeneous ...

and smart contracts across different blockchain networks. Qubetics enables developers to reach a wider audience and address more use cases by making it easier to build on multiple chains. Some of the ...