Ultrasensitive MicroRNA Detection Combining Reduced Graphene Oxide Electrolyte‐Gated Transistors and Machine Learning

MicroRNAs (miRNAs) are promising biomarkers for disease diagnosis, but conventional detection methods such as reverse transcription polymerase chain reaction (RT-PCR) require complex instrumentation and reagents, limiting their suitability for portable diagnostics. Here, we report an ultrasensitive and selective biosensor that integrates DNA-functionalized reduced graphene oxide (rGO), electrolyte-gated transistors (EGTs), and machine learning (ML) for miRNA detection. The platform targets the miR-34 family (miR-34a, miR-34b, and miR-34c), which is associated with cancer and neurological disorders. The biosensor discriminates perfectly matched from mismatched sequences over a wide dynamic range (0.1-1000 amol L^-1) with an ultralow limit of detection of 0.098 amol L^-1. ML enables multidimensional analysis of EGT transfer curves and extraction of physically meaningful features from high-dimensional data. This approach advances point-of-care technologies for highly sensitive and selective miRNA detection, with strong potential for portable molecular diagnostics.