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A method for correcting errors in the spatial-genetic reconstruction of DNA microscopy is proposed, leading to more accurate results and potential to resolve new biology.
DNA microscopy reconstructs the spatial organization of a sample from a neighborhood graph. In this work, MinIPath efficiently corrects errors from these graphs that distort the reconstruction, both in simulated and experimental data.
Language models offer promises in encoding quantum correlations and learning complex quantum states. This Perspective discusses the advantages of employing language models in quantum simulation, explores recent model developments, and offers insights into opportunities for realizing scalable and accurate quantum simulation.
The laws of physics, formulated in a compact form, are elusive for complex dynamic phenomena. However, it is now shown that, using artificial intelligence constrained by the physical Onsager principle, a custom thermodynamic description of a complex system can be constructed from the observation of its dynamical behavior.
The authors develop a general method that combines machine learning and physics to construct macroscopic dynamics directly from microscopic observations, leading to an intuitive understanding of polymer stretching in elongational flow.
A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.
A mathematical framework that allows computing the input–output function of neurons with active dendrites reveals how dendrites readily and potently control the response variability, a result that is experimentally confirmed.
EmerGNN is a flow-based graph neural network (GNN) approach that advances on conventional methodologies for predicting drug–drug interactions in emerging drugs by effectively leveraging biomedical networks.
EmerGNN, a method to predict interactions for emerging drugs, may improve patient care and drug development by providing insight into the effects of using biomedical networks in interaction predictions.
Transformer methods are revolutionizing how computers process human language. Exploiting the structural similarity between human lives, seen as sequences of events, and natural-language sentences, a transformer method — dubbed life2vec — has been used to create rich vector representations of human lives, from which accurate predictions can be made.
Using registry data from Denmark, Lehmann et al. create individual-level trajectories of events related to health, education, occupation, income and address, and also apply transformer models to build rich embeddings of life-events and to predict outcomes ranging from time of death to personality.
A diffusion model that generates chemical reactions in 3D with all desired symmetries preserved is established and shown to reduce transition state search from days to seconds and complement intuition-based reaction exploration with generative AI.