Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Jan. 19, 2024 — Solving today’s most complex scientific challenges often means tracing links between hundreds, thousands or even millions of variables. The larger the scientific dataset, the more ...

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TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench—a powerful toolkit that enables data scientists to significantly improve ML ...

Gene regulatory networks (GRNs) depict the regulatory mechanisms of genes within cellular systems as a network, offering vital insights for understanding cell processes and molecular interactions that ...

How do single cells communicate in a tissue? How can these interactions be modeled, while retaining information of spatial context? Researchers around Fabian Theis from Helmholtz Munich Computational ...

How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in ...

A group of scientists has created a neural network based on polymeric memristors -- devices that can potentially be used to build fundamentally new computers. These developments will primarily help in ...