Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning-Reference-Cited by-同舟云学术

Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning

Published:2019 Issue:14 Volume:144 Page:4312-4319
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Chen Xuejing¹²³⁴⁵,Xie Luyuan¹²³⁴⁵,He Yonghong¹²³⁴⁵^ORCID,Guan Tian¹²³⁴⁵,Zhou Xuesi¹²³⁴⁵,Wang Bei¹²³⁴⁵,Feng Guangxia¹²³⁴⁵,Yu Haihong⁶⁷⁸⁹¹⁰,Ji Yanhong¹¹⁸¹²¹⁰

Affiliation:

1. Shenzhen Key Laboratory for Minimal Invasive Medical Technologies

2. Institute of Optical Imaging and Sensing

3. Graduate School at Shenzhen

4. Tsinghua University

5. Shenzhen 518055

6. MOE Key Laboratory of Laser Life Science & SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology

7. College of Biophotonics

8. South China Normal University

9. Guangzhou 510631

10. China

11. School of Physics and Telecommunication Engineering

12. Guangzhou 510006

Abstract

A deep learning network called “residual neural network” (ResNet) was used to decode Raman spectra-encoded suspension arrays (SAs).

Funder

National Natural Science Foundation of China

Tsinghua University

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/AN/C9AN00913B

Reference38 articles.

1. Highly Efficient Preparation of Multiscaled Quantum Dot Barcodes for Multiplexed Hepatitis B Detection

2. High throughput differential identification of TMPRSS2-ERG fusion genes in prostate cancer patient urine

3. Application of suspension array for simultaneous detection of four different mycotoxins in corn and peanut

4. Ultrasensitive low-background multiplex mycotoxin chemiluminescence immunoassay by silica-hydrogel photonic crystal microsphere suspension arrays in cereal samples

5. Highly specific detection of thrombin using an aptamer-based suspension array and the interaction analysis via microscale thermophoresis

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