Machine-learning based classification of 2D-IR liquid biopsies enables stratification of melanoma relapse risk.

ABSTRACT: Non-linear laser spectroscopy methods such as two-dimensional infrared (2D-IR) produce large, information-rich datasets, while developments in laser technology have brought substantial increases in data collection rates. This combination of data depth and quantity creates the opportunity to unite advanced data science approaches, such as Machine Learning (ML), with 2D-IR to reveal insights that surpass those from established data interpretation methods. To demonstrate this, we show that ML and 2D-IR spectroscopy can classify blood serum samples collected from patients with melanoma according to diagnostically-relevant groupings. Using just 20 μL samples, 2D-IR measures 'protein amide I fingerprints', which reflect the protein profile of blood serum. A hyphenated Partial Least Squares-Suppo

SUBMITTER: Brown K 

PROVIDER: S-EPMC11983777 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature