Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes^†

Abstract

Comprehensive SummaryThe electrical tunneling sensors have excellent potential in the next generation of single‐molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution capabilities. Electrical tunneling signals that have been measured at a high sampling rate may provide detailed molecular information. Despite the extraordinarily large amount of data that has been gathered, it is still difficult to correlate signal transformations with molecular processes, which creates great obstacles for signal analysis. Machine learning is an effective tool for data analysis that is currently gaining more significance. It has demonstrated promising results when used to analyze data from single‐molecule electrical measurements. In order to extract meaningful information from raw measurement data, we have combined intelligent machine learning with tunneling electrical signals. For the purpose of analyzing tunneling electrical signals, we investigated the clustering approach, which is a classic algorithm in machine learning. A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model clustering. Additionally, customized statistical algorithms were designed. It has been proved to efficiently gather molecular information and enhance the effectiveness of data analysis.This article is protected by copyright. All rights reserved.

Molecular-Scale Electronics: From Individual Molecule Detection to the Application of Recognition Sensing

“…However, employing intelligent machine learning techniques to analyze electrical tunneling signals has significantly enhanced the efficiency of data analysis and the understanding of molecular information. 109 Table 2 presents an overview of the triple-molecule systems utilized for biosensing, wherein analyte detection relies on weak interactions with linker molecules. Notably, the bioelectronic device based on the triple-molecule system can play a critical role in diagnostics and the analysis of protein function.…”

“…Second, current fabrication methods for SMJs often have limited reproducibility, unreliable contact, or inadequate binding of analytes onto the electrodes for reliable long-term measurement. , Third, achieving precise trapping, positioning, and orienting of target analytes within the nanogap of electrodes remains a persistent challenge, critical for consistent and accurate measurements. Additionally, while electrical tunneling sensors hold immense potential in next-generation sequencing technologies, correlating signal transformations with specific molecular processes presents obstacles in signal analysis …”

“…To overcome these challenges, ongoing research is focusing on improving fabrication protocols of SMJs, optimizing electrode design for enhanced contact and stability using materials such as graphene and silicon, − developing innovative strategies for precise analyte trapping and orientation, , as well as innovating the statistical algorithms for data analysis . Collaborative interdisciplinary work among researchers in nanoscience, analytical chemistry, and biotechnology is essential for unlocking the full potential of SMJs and driving innovation in the analytical sciences.…”

“…Nevertheless, the large amount of data collected from single-molecule electrical measurements, coupled with its complexity, poses challenges in correlating signal transformations with molecular processes, thus impeding signal analysis. However, employing intelligent machine learning techniques to analyze electrical tunneling signals has significantly enhanced the efficiency of data analysis and the understanding of molecular information …”