GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search ...

In October 2024 I attended a workshop at Harvard University where mathematicians talked through the uses of artificial intelligence in their field. Most were less worried about the future of math than ...

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pggb builds pangenome variation graphs from a set of input sequences. A pangenome variation graph is a kind of generic multiple sequence alignment. It lets us understand any kind of sequence variation ...

Graph neural networks (GNNs) have been applied with great success across science and engineering, but we do not understand why they work so well. Motivated by experimental evidence of a rich phase ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It ...

Abstract: Graph prediction problems prevail in data analysis and machine learning. The inverse prediction problem, namely to infer input data from given output labels, is of emerging interest in ...

Definitive answers to the big questions. Credit...Photo Illustration by Andrea D'Aquino Supported by By Julia Rosen Ms. Rosen is a journalist with a Ph.D. in geology. Her research involved studying ...

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment ...