Machine learning-aided protein identification from multidimensional signatures-Reference-Cited by-同舟云学术

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Machine learning-aided protein identification from multidimensional signatures

Published:2021 Issue:15 Volume:21 Page:2922-2931
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Zhang Yuewen¹²³⁴^ORCID,Wright Maya A.¹²³⁴,Saar Kadi L.¹²³⁴⁵^ORCID,Challa Pavankumar¹²³⁴,Morgunov Alexey S.¹²³⁴⁶,Peter Quentin A. E.¹²³⁴^ORCID,Devenish Sean⁶⁷⁴,Dobson Christopher M.¹²³⁴,Knowles Tuomas P. J.¹²³⁴⁵^ORCID

Affiliation:

1. Yusuf Hamied Department of Chemistry

2. University of Cambridge

3. Cambridge CB2 1EW

4. UK

5. Cavendish Laboratory

6. Fluidic Analytics Ltd.

7. Cambridge

Abstract

Protein classification and identification from their multidimensional fingerprints obtained on a microfluidic chip.

Funder

H2020 European Research Council

Engineering and Physical Sciences Research Council

Biotechnology and Biological Sciences Research Council

Seventh Framework Programme

Frances and Augustus Newman Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2021/LC/D0LC01148G

Reference41 articles.

1. The Cell as a Collection of Protein Machines: Preparing the Next Generation of Molecular Biologists

2. A vision for the future of genomics research

3. Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications;Lab on a Chip;2024

2. Protein classification by autofluorescence spectral shape analysis using machine learning;Talanta;2024-01

3. Recent Advances in Microgels: From Biomolecules to Functionality;Small;2022-07-05

4. Diffusional microfluidics for protein analysis;TrAC Trends in Analytical Chemistry;2022-01

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