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Title: Where nanosensors meet machine learning: prospects and challenges in detecting Disease X
Authors: Leong, Yong Xiang
Tan, Emily Xi
Leong, Shi Xuan
Koh, Charlynn Sher Lin
Nguyen, Lam Bang Thanh
Chen, Jaslyn Ru Ting
Xia, Kelin
Ling, Xing Yi
Keywords: Science::Chemistry::Biochemistry
Issue Date: 2022
Source: Leong, Y. X., Tan, E. X., Leong, S. X., Koh, C. S. L., Nguyen, L. B. T., Chen, J. R. T., Xia, K. & Ling, X. Y. (2022). Where nanosensors meet machine learning: prospects and challenges in detecting Disease X. ACS Nano, 16(9), 13279-13293.
Journal: ACS Nano 
Abstract: Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.
ISSN: 1936-0851
DOI: 10.1021/acsnano.2c05731
Schools: School of Physical and Mathematical Sciences 
School of Chemistry, Chemical Engineering and Biotechnology 
Rights: This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Nano, copyright © 2022 American Chemical Society, after peer review and technical editing by the publisher. To access the final edited and published work see
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CCEB Journal Articles
SPMS Journal Articles

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