A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...
Heterogeneous Graph Neural Networks (HetGNN) have garnered significant attention and demonstrated success in tackling various tasks. However, most existing HetGNNs face challenges in effectively ...
Katherine Chui, a graphics reporter, analyzed two centuries of census and congressional data. Emily Cochrane is based in Nashville and covers the American South. April 30, 2026 The central tenet of ...
Got Tech, Data, AI and Media, and not afraid to use them. Gartner highlighted Data Management, Semantic Layers, and GraphRAG as Top Trends in Data and Analytics for 2026. Startups and incumbents in ...
The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design. In addition, Nature has recently ...
Abstract: Representing and exploiting multivariate signals requires capturing relations between variables, which we can represent by graphs. Graph dictionaries allow to describe complex relational ...
Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing the development of drugs. While existing in-silico methods leverage direct ...
Representing the brain as a complex network typically involves approximations of both biological detail and network structure. Here, we discuss the sort of biological detail that may improve network ...
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