Machine Learning to Treat Data for the Design and Improvement of Electrochemical Sensors: Application for a Cancer Biomarker

Label-free immunosensors based on screen-printed carbon electrodes offer a promising platform for the detection of cancer biomarkers. Herein, we explore the use of machine learning techniques to improve the performance of these immunosensors. We evaluate the influence of various redox probes on the analytical response in detecting the cancer biomarker protein p53. Ascorbic acid (AA) was found as the optimal redox probe, exhibiting a sensitivity of 0.26 ng mL-1, attributed to its strong affinity to proteins through hydrogen bonds and electrostatic interactions. We also extracted analytical information from the voltammograms, such as shifts in peak potential and changes in peak width, to construct datasets for supervised machine learning. Using different algorithms including logistic regression, linear discriminant analysis, K-nearest neighbor, Gaussian Naive-Bayes, decision trees, and support vector machine, we identified positive samples spiked with p53 in artificial urine and saliva samples. Through a comparison of immunosensors with distinct molecular architectures, we determined the critical role of redox probe selection, which proves to be more significant than modifying the working electrodes in determining performance. Furthermore, immunosensors with inferior inherent detection ability can achieve comparable performance to those with superior analytical characteristics when feature selection and machine learning algorithms are applied to the voltammograms. These findings illustrate the significance of extracting additional information from differential pulse voltammograms beyond peak current intensity. Furthermore, using machine learning techniques allows one to design biosensors capable of distinguishing biomarkers even in complex samples.