High-Throughput Recognition of Tumor Cells Using Label-Free Elemental Characteristics Based on Interpretable Deep Learning-Reference-Cited by-同舟云学术

High-Throughput Recognition of Tumor Cells Using Label-Free Elemental Characteristics Based on Interpretable Deep Learning

Published:2022-02-07 Issue:7 Volume:94 Page:3158-3164
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Chen Youyuan¹,Yin Pengkun¹,Peng Zhengying¹,Lin Qingyu²^ORCID,Duan Yixiang²^ORCID,Fan Qingwen²,Wei Zhimei³⁴

Affiliation:

1. Key Laboratory of Bio-Resource and Eco-Environment, Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610064, P. R. China

2. Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu 610064, P. R. China

3. Institute of Materials Science and Technology, Analysis and Testing Center, Sichuan University, Chengdu 610064, P. R. China

4. State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, P. R. China

Funder

State Key Laboratory of Polymer Materials Engineering

Department of Science and Technology of Sichuan Province

Chengdu technological innovation Research and Development project

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.1c04553

Reference37 articles.

1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

2. Circulating tumor cell detection methods in renal cell carcinoma: A systematic review

3. Are trace element concentrations suitable biomarkers for the diagnosis of cancer?

4. Human Cancer Classification: A Systems Biology- Based Model Integrating Morphology, Cancer Stem Cells, Proteomics, and Genomics

5. Developing a fluorescence immunosensor for detection of HER2-positive breast cancer based on graphene and magnetic nanoparticles

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