The divergence of ASIC designs makes it difficult to run commonly used modern sequencing analysis pipelines due to software ...

Here, we showcase how our graph neural network (GNN)-based implicit solvent (GNNIS ... for which the prediction diverges from the linear trend. However, the explicit-solvent simulations of compound I1 ...

Abstract: Graph neural networks (GNNs) are capable of modeling graph data using various types of nodes and edges, and thus can be widely used in the fields of recommender systems and bioinformatics.

Upon cardiac injury, they manifest a more prominent cardiac remodeling and enhanced metabolism that help maintain cardiac function and promote systemic recovery. It will be interesting to explore ...

For each pattern, we construct an independent adaptive spatio-temporal fusion graph based on a cross-attention mechanism, employing residual graph convolution modules and time series modules to better ...

Linear functions ... graphs and equations there are 3 key concepts: To determine if this resource will benefit you, start by answering the following questions. Have you ever encountered a situation ...

In the rehabilitation of limb motor function ... based on changes in brain network parameters to guide accurate rTMS stimulation programs. Method: Thirty-six patients with stroke were selected and ...

These are polynomial functions, for the Poisson equation ... Weinert’s pseudo-charge method is based on the observation that the relation between the charge density inside a sphere and its multipole ...

We have developed a fully synthetic lung surfactant preparation, based on designed analogues of surfactant proteins SP-B and SP-C, for treatment of respiratory distress syndrome in premature infants.