Machine Learning-Assisted Pattern Recognition of Amyloid Beta Aggregates with Fluorescent Conjugated Polymers and Graphite Oxide Electrostatic Complexes.

Five fluorescent positively charged poly(para-aryleneethynylene) (P1-P5) were designed to construct electrostatic complexes C1-C5 with negatively charged graphene oxide (GO). The fluorescence of conjugated polymers was quenched by the quencher GO. Three electrostatic complexes were enough to distinguish between 12 proteins with 100% accuracy. Furthermore, using these sensor arrays, we could identify the levels of Aβ40 and Aβ42 aggregates (monomers, oligomers, and fibrils) via employing machine learning algorithms, making it an attractive strategy for early diagnosis of Alzheimer's disease.