Machine learning and its applications for plasmonics in biology

Abstract

Machine learning (ML) has drawn tremendous interest for its capac-ity to extract useful information that may be overlooked with con-ventional analysis techniques and for its versatility in a wide range of research domains, including biomedical sensing and imaging. In this perspective, we provide an overview focused on the uses and benefits of ML in areas of plasmonics in biology. ML methodologies for processing data from plasmonic biosensing and imaging systems by supervised and unsupervised learning to achieve enhanced detection and quantification of target analytes are described. In addition, deep learning-based approaches to improve the design of plasmonic structures are presented. Data analysis based on ML for classification, regression, and clustering by dimension reduction is presented. We also discuss ML-based prediction and design of plasmonic structures and sensors using discriminative and genera-tive models. Challenges and the outlook for ML for plasmonics in biology are summarized. Based on these insights, we are convinced that ML can add value to plasmonics techniques in biological sensing and imaging applications to make them more powerful with improved detection and resolution.