Deep learning in single-molecule imaging and analysis recent advances and prospects
Single-molecule microscopy is advantageous to characterizing heterogeneous dynamics on the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest in single-molecule data. As a new class of computer algorithms that simulates the human brain to extract data features, deep learning network excels in task parallelism, model generalization, and is well-suited for handling nonlinear functions and extracting weak features, which provides a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advance in the applications of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.