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. Researchers ...
A proteomics data pipeline transforms raw mass spectrometry spectra into biologically interpretable protein-level ...
A machine learning model has been developed that makes optical spectroscopy data easier and quicker to interpret. Researchers from Rice University (TX, USA) have developed a new machine learning ...
The SpecCLIP model acts as a translator that can convert LAMOST's low-resolution spectra and Gaia's high-precision spectra into a universal language, allowing scientists to perform joint analyses with ...
A Chinese research team has developed an artificial intelligence (AI) model called SpecCLIP, which can interpret stellar spectral data from different telescopes, demonstrating the vast potential of AI ...
Workflow of the proposed AI-based approach interpreting X-ray absorption spectroscopy (XAS) data (IMAGE) ...
In this interview, Kevin Broadbelt of Thermo Fisher Scientific discusses the small molecule applications of process Raman spectroscopy. How do cell therapies differ in complexity compared to ...
Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...
Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results