Nature

Graph-based deep learning approach for high-throughput protein-DNA interaction scoring

Accurately quantifying protein-DNA interactions (PDIs) is critical for understanding biological processes and facilitating drug design. However, the inherent flexibility of nucleic acids limits the ...
Nature

Temporal influence maximization via continuous-time graph neural networks and deep reinforcement learning

Influence maximization (IM) seeks to identify a subset of key nodes that maximize the spread of information or behavior through a network. While traditional IM approaches rely on static topologies or ...
EurekAlert!

Multi-source graph learning may improve drug–target affinity prediction

Figure 1. Overall framework of MIGDTA. GCN: graph convolutional network; GIN: graph isomorphism network; CNN: convolutional neural network; MLP: multi-layer ...
Frontiers

Numerical foundations for scalable statistical and machine learning methods

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...