Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images Using Machine Learning.

ABSTRACT: Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUC). While Gaussian spectral fitting for one spectrum is viable, e

SUBMITTER: Edmonds H 

PROVIDER: S-EPMC12353105 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature