AI in Measurement Science

• Published in: Annual review of analytical chemistry (Palo Alto, Calif.)
• Main Authors: Liu, Chao, Sun, Jiashu
• Format: Journal Article
• Language: English
• Published: United States 27.07.2021
• ISSN: 1936-1335
• DOI: 10.1146/annurev-anchem-091520-091450

Measurement of biological systems containing biomolecules and bioparticles is a key task in the fields of analytical chemistry, biology, and medicine. Driven by the complex nature of biological systems and unprecedented amounts of measurement data, artificial intelligence (AI) in measurement science has rapidly advanced from the use of silicon-based machine learning (ML) for data mining to the development of molecular computing with improved sensitivity and accuracy. This review presents an overview of fundamental ML methodologies and discusses their applications in disease diagnostics, biomarker discovery, and imaging analysis. We next provide the working principles of molecular computing using logic gates and arithmetical devices, which can be employed for in situ detection, computation, and signal transduction for biological systems. This review concludes by summarizing the strengths and limitations of AI-involved biological measurement in fundamental and applied research. Expected final online publication date for the , Volume 14 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.