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Gene regulatory network inference based on ca - Vimarsana News

Gene regulatory network inference based on ca

<p>The work provides a new clue for constructing GRNs, and&nbsp;proposed framework GRINCD also shows potential in identifying key factors affecting cancer development.</p>

Advancing gene regulatory network inference with causal discovery and graph neural networks - Vimarsana News

Advancing gene regulatory network inference with causal discovery and graph neural networks

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 determine cellular phenotypes.

Apple Silicon gets massive AI training speed boost with this new project - macOS Discussions on AppleInsider Forums - Vimarsana News

Apple Silicon gets massive AI training speed boost with this new project - macOS Discussions on AppleInsider Forums

A new project to improve the processing speed of neural networks on Apple Silicon is potentially able to speed up training on large datasets by up to ten times.

Graph training project for Apple Silicon helped by mlx-graphs - Vimarsana News

Graph training project for Apple Silicon helped by mlx-graphs

A new project to improve the processing speed of neural networks on Apple Silicon is potentially able to speed up training on large datasets by up to ten times.

University of Amsterdam: Four projects kick-start the Artificial Intelligence for Sustainable Molecules and Materials research programme - Vimarsana News

University of Amsterdam: Four projects kick-start the Artificial Intelligence for Sustainable Molecules and Materials research programme

To kick-start the new Research Priority Area (RPA) of the Faculty of Science in "Artificial Intelligence for Sustainable Molecules and Materials" (AI4SMM), four projects have just been launched. Combining the Faculty’s strengths in computer science and sustainability research, AI4SMM aims to develop and use machine learning techniques for the design of molecules and materials for a sustainable future. The projects cover topics as diverse as metamaterials for sustainable steel, and protein mixtures for sustainable food design.