In biology textbooks and beyond, the human genome and DNA therein typically are taught in only one dimension. While it can be ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

Caltech scientists have found a fast and efficient way to add up large numbers of Feynman diagrams, the simple drawings ...

We compare the proposed algorithm with other state-of-the-art graph matching algorithms based on a single-layer structure using synthetic and real data sets and demonstrate the superior performance of ...

Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured ...

Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue ...