Light signature algorithm offers precise insight on viral proteins, brain disease markers and semiconductors

Light signature algorithm offers precise insight on viral proteins, brain disease markers and semiconductors

Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the "light signatures" (optical spectra) of molecules, materials and disease biomarkers, potentially enabling faster and more precise medical diagnoses and sample analysis.

New AI algorithm sharpening the focus on light-based data analysis

A new machine learning method from Rice University helps scientists better understand the unique light signatures of molecules and materials. This AI algorithm breakthrough at Rice University offers clearer, faster analysis for medical and scientific applications A team at Rice University has unveiled a novel machine learning (ML) algorithm poised to revolutionise the interpretation of molecular light signatures. This breakthrough promises to en…

New Machine Learning Technique Enhances Clarity of Light-Based Data

In a groundbreaking advancement at Rice University, researchers have unveiled a pioneering machine learning algorithm designed to revolutionize the interpretation of optical spectroscopy data. This new technology, termed Peak-Sensitive Elastic-net Logistic Regression (PSE-LR), promises unprecedented precision in analyzing the subtle and complex “light signatures” emitted by molecules, materials, and biological samples. The implications for medic…

Light signature algorithm offers precise insight on viral proteins, brain disease markers and semiconductors – Sky News: The Latest News from the World

Ziyang Wang and Shengxi Huang. Credit: Jeff Fitlow/ Rice University Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the “light signatures” (optical spectra) of molecules, materials and disease biomarkers, potentially enabling faster and more precise medical diagnoses and sample analysis. “Imagine being able to detect early signs […]