A Universal Method for Fingerprinting Multiplexed Bacteria: Evolving Pruned Sensor Arrays via Machine Learning-Driven Combinatorial Group-Specificity Strategy

Abstract

Array-based sensing technology holds immense potential for discerning the intricacies of biological systems. Nevertheless, developing a universal strategy for simultaneous identification of diverse types of multianalytes and meeting the diagnostic needs of a range of multiclassified clinical diseases poses substantial challenges. Herein, we introduce a combination method for constructing sensor arrays by assembling two types of group-specific elements. Such a method enables the rapid generation of a library of 100 sensing units, each with dual bacterial targeting capabilities. By employing a three-step screening strategy optimized by machine learning algorithms, various optimal five-element arrays were rapidly obtained for diverse clinical infectious models. Moreover, the pruned arrays successfully identified disparate mixing ratios and quantitative detection of clinically prevalent bacterial strains. Optimized through nine multiclassification algorithms, the top-performing multilayer perceptron (MLP) model demonstrated impressive recognition capabilities, achieving 100% accuracy for diagnosing clinical urinary tract infection (UTI) and 99.4% accuracy for clinical sepsis detection in the test models we collected. Such a combinatorial library construction and screening process should be standard and provides insights into successfully generating powerful high-recognition sensor elements and configuring them into highly discriminative mini-sensor arrays.