Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
kmindex is a tool able to index thousands of environmental metagenomes and perform sequence searches in a fraction of a second, thus enabling real-time queries on complex genomic datasets.
We highlight the challenges and opportunities in organic redox flow battery research, underscoring the need for collaborative research efforts. The synergy between computation and experimentation holds the potential to expedite progress in this field and can have far-reaching impacts beyond energy storage applications.
One of the greatest limitations of deep neural networks is the difficulty of interpreting what they learn from the data. Deep distilling addresses this problem by extracting human-comprehensible and executable code from observations.
Automated algorithm discovery has been difficult for artificial intelligence given the immense search space of possible functions. Here explainable neural networks are used to discover algorithms that outperform those designed by humans.
The authors introduce two cellular barcoding tools: CellBarcode, for extracting and filtering diverse DNA barcodes from bulk and single-cell sequencing data; and CellBarcodeSim, for simulating barcoding experiments, thus enabling the investigation of the impact of biological and technical factors on barcode detection.
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.
