Accurate diagnosis of hemoglobinopathies with machine learning based on high-throughput proteomics.

ABSTRACT: Hemoglobinopathies, such as sickle cell disease and thalassemias, impose a substantial global burden, particularly in endemic regions. Current diagnostic methods, such as high-performance liquid chromatography (HPLC), capillary electrophoresis, and genetic testing, can be time-consuming, expensive, or limited in detecting all variants. This study introduces a novel diagnostic framework that combines high-throughput proteomics with machine learning to address these challenges. We processed red blood cells, whole blood, and plasma samples from 82 individuals (development cohort) and 45 individuals (validation cohort) with structural hemoglobin variants (hemoglobin S, hemoglobin C, hemoglobin D, and hemoglobin E) or β-thalassemia trait, as confirmed by standard clinical testing. Tryptic pepti

SUBMITTER: Wei S 

PROVIDER: S-EPMC12606001 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature