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Keyword: graph neural networks ×Clear all

7 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Advanced Electron Microscopy Techniques and Applications

   Automated model building and protein identification in cryo-EM maps

   Jamali, Käll, Zhang, Brown, Kimanius and Scheres · Nature · 2024

   The paper presents ModelAngelo, a machine-learning method that automatically builds atomic models in cryo-EM maps and identifies proteins of unknown sequence, matching or outperforming human experts.

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   1. UncheckedModelAngelo, a graph neural network using map, sequence and structure information, builds protein atomic models of similar quality to human experts.“By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts.”
   2. UncheckedFor nucleic acids, the ModelAngelo software builds backbone structures in cryo-EM maps with accuracy similar to those built by human experts.“For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans.”
   3. UncheckedModelAngelo, using predicted amino acid probabilities in sequence searches, identifies proteins of unknown sequence better than human experts do.“By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Forecasting Global Weather with Graph Neural Networks

   Keisler · arXiv (Cornell University) · 2022

   A graph neural network learns to advance the global 3D atmospheric state by six hours, and chaining steps gives skilful forecasts several days ahead, trained on ERA5 reanalysis or GFS forecast data.

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   1. UncheckedA graph neural network weather model matches operational GFS and ECMWF forecasts on Z500 and T850 at 1-degree scales, using reanalysis starting conditions.“Test performance on metrics such as Z500 (geopotential height) and T850 (temperature) improves upon previous data-driven approaches and is comparable to operational, full-resolution, physical models from GFS and ECMWF, at least when evaluated on 1-degree scales and when using reanalysis initial con…”

3. Materials Science › Machine Learning in Materials Science

   Physically Informed Machine Learning Prediction of Electronic Density of States

   Fung, Ganesh and Sumpter · Chemistry of Materials · 2022

   The paper presents a graph neural network that predicts electronic density of states from atomic positions as a fast stand-in for DFT, with a scheme that nudges predictions towards physically reasonable results.

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   1. UncheckedThe authors built a graph neural network that predicts a material's electronic density of states from atomic positions alone, about a million times faster than DFT.“To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT.”
   2. UncheckedThe authors say their graph neural network method for predicting electronic density of states can use large databases and apply to any element and material type.“This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity.”
   3. Unchecked“We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints.”