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EMBL-EBI scientists and collaborators at Heidelberg University have developed CORNETO, a new computational tool that uses machine learning to gain meaningful insights from complex biological data.
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AZoRobotics on MSNNASA Uses AI to Sharpen Metadata Tagging for Earth Science DatasetsThe enhanced GKR tool from NASA leverages AI to optimize keyword tagging, addressing metadata challenges and facilitating ...
Matthew Leming, Ph.D., and Hyungsoon Im, Ph.D. of the Center for Systems Biology at Massachusetts General Hospital, are the ...
Ground-breaking, GPU-free machine learning now installs in minutes on Ubuntu, Red Hat Enterprise Linux, and Fedora, complete with a 30-day free tr ...
Machine learning helps improve accuracy and efficiency of small-molecule calculations Microsoft researchers used deep learning to create new DFT model by Sam Lemonick, special to C&EN June 20, 2025 ...
The Vera C. Rubin Observatory will make the study of stars and galaxies more like the big data-sorting exercises of contemporary genetics and particle physics.
Data Science Agent uses Anthropic PBC’s Claude large language model to dissect machine learning projects into logical steps and deliver executable pipeline components that can be run inside ...
Discover what data science is, its benefits, techniques, and real-world use cases in this comprehensive guide. Data science merges statistics, science, computing, machine learning, and other ...
Databricks edged out the 'Big 3' cloud giants in the latest Gartner Magic Quadrant for Data Science and Machine Learning Platforms, which itself mirrors the industry shift to agentic AI, emphasizing ...
Researchers have developed a new machine learning algorithm that excels at interpreting optical spectra, potentially enabling faster and more precise medical diagnoses and sample analysis.
After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help ...
A new scientific machine learning framework developed by Professors Horacio D. Espinosa, Sridhar Krishnaswamy, and collaborators accurately predicts and inversely designs the mechanical behavior of ...
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