A global team mapped over 100,000 structural variants in human genomes by applying Oxford Nanopore long-read sequencing and a ...

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Sigmetrix champions early-stage tolerance analysis to help engineers create manufacturable, high performance products without ...

In this work, we develop a family of Aligned Entropic Graph Kernels (AEGK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph structure, and ...

Variation graphs can become very large and complicated and it is often useful to break down the graph and describe the commonly occurring nested substructures. Variations in the graph often have a ...

MicroCloud Hologram Inc. Researches CV-QNN (Continuous Variable Quantum Neural Networks) Technology and Builds Variational Quantum Circuits Embedded in CV Architecture ...

Researchers found variation in continuous glucose monitoring (CGM), with glycemic index for a commercial fruit smoothie higher with CGM vs capillary sampling. HealthDay News — Continuous glucose ...

The real breakthrough here, however, is the continuous, non-invasive monitoring that could enable early detection of conditions that usually go unnoticed until it’s too late.

Think about a toolbox for a moment. You have different tools for different jobs. A screwdriver makes a poor hammer, for example. Having the right tool for the right job is just the way things work. So ...

Variation graphs A variation graph is a sequence graph (the nodes and edges) and a collection of haplotype paths through the graph. The sequence graph model used by the vg toolkit is a bi-directed ...