Where nanosensors meet machine learning: prospects and challenges in detecting Disease X
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 a...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/161645 |
| _version_ | 1826127743640666112 |
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| author | 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 |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences 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 |
| author_sort | Leong, Yong Xiang |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:13:52Z |
| format | Journal Article |
| id | ntu-10356/161645 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:13:52Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1616452023-06-21T08:28:36Z Where nanosensors meet machine learning: prospects and challenges in detecting Disease X 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 School of Physical and Mathematical Sciences School of Chemistry, Chemical Engineering and Biotechnology Science::Chemistry::Biochemistry Nanosensors Nanomaterials 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. Nanyang Technological University Submitted/Accepted version S.X.L. and N.B.T.L. acknowledge Nanyang President’s Graduate Scholarship support from Nanyang Technological University, Singapore. 2022-09-13T05:20:56Z 2022-09-13T05:20:56Z 2022 Journal Article 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. https://dx.doi.org/10.1021/acsnano.2c05731 1936-0851 https://hdl.handle.net/10356/161645 10.1021/acsnano.2c05731 36067337 9 16 13279 13293 en ACS Nano 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 https://doi.org/10.1021/acsnano.2c05731. application/pdf |
| spellingShingle | Science::Chemistry::Biochemistry Nanosensors Nanomaterials 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 Where nanosensors meet machine learning: prospects and challenges in detecting Disease X |
| title | Where nanosensors meet machine learning: prospects and challenges in detecting Disease X |
| title_full | Where nanosensors meet machine learning: prospects and challenges in detecting Disease X |
| title_fullStr | Where nanosensors meet machine learning: prospects and challenges in detecting Disease X |
| title_full_unstemmed | Where nanosensors meet machine learning: prospects and challenges in detecting Disease X |
| title_short | Where nanosensors meet machine learning: prospects and challenges in detecting Disease X |
| title_sort | where nanosensors meet machine learning prospects and challenges in detecting disease x |
| topic | Science::Chemistry::Biochemistry Nanosensors Nanomaterials |
| url | https://hdl.handle.net/10356/161645 |
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