Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases

Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Wang, Deli; Song, Youtao; Yu, Kun

doi:10.1002/jrs.4924

Cited by 53 publications

(27 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other data mining techniques, such as support vector machines (SVM), genetic algorithms, discrimination trees (DTs), or artificial neural networks can be very powerful for class separation but are more difficult to relate to the underlying biology . Tree classifiers, also known as DTs, have been less widely used, and even though they are sometimes less powerful than the previously mentioned classifiers, their output is easier to relate to the original spectral features, and they can also capture non‐linear relationships within the data. SVM are very powerful classification methods, but it is sometimes difficult to extract useful knowledge from the trained models.…”

Section: Introductionmentioning

confidence: 99%

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Pablo

Armistead

Peyman

et al. 2018

J Raman Spectroscopy

View full text Add to dashboard Cite

Label‐free live single‐cell Raman spectroscopy was used to obtain a chemical fingerprint of colorectal cancer cells including the classification of the SW480 and SW620 cell line model system, derived from primary and secondary tumour cells from the same patient. High‐quality Raman spectra were acquired from hundreds of live cells, showing high reproducibility between experiments. Principal component analysis with linear discriminant analysis yielded the best cell classification, with an accuracy of 98.7 ± 0.3% (standard error) when compared with discrimination trees or support vector machines. SW480 showed higher content of the disordered secondary protein structure Amide III band, whereas SW620 showed larger α‐helix and β‐sheet band content. The SW620 cell line also displayed higher nucleic acid, phosphates, saccharide, and CH 2 content. HL60, HT29, HCT116, SW620, and SW480 live single‐cell spectra were classified using principal component analysis or linear discriminant analysis with an accuracy of 92.4 ± 0.4% (standard error), showing differences mainly in the β‐sheet content, the cytochrome C bands, the CH‐stretching regions, the lactate contributions, and the DNA content. The lipids contributions above 2,900 cm −1 and the lactate contributions at 1,785 cm −1 appeared to be dependent on the colorectal adenocarcinoma stage, the advanced stage cell lines showing lower lipid, and higher lactate content. The results demonstrate that these cell lines can be distinguished with high confidence, suggesting that Raman spectroscopy on live cells can distinguish between different disease stages, and could play an important role clinically as a diagnostic tool for cell phenotyping.

show abstract

Section: Introductionmentioning

confidence: 99%

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Pablo

Armistead

Peyman

et al. 2018

J Raman Spectroscopy

View full text Add to dashboard Cite

show abstract

“…Through experiments, we found that Raman peaks and infrared peaks are caused by biomolecules such as proteins, nucleic acids and lipids, and the intensity may reflect the corresponding molecular concentration [19]. Thyroid dysfunction causes changes in the blood composition, which will be ultimately shown as changes in peak intensity and new peaks appearance [20].…”

Section: Discussionmentioning

confidence: 94%

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

Chen

Tong

et al. 2019

Journal of Biophotonics

View full text Add to dashboard Cite

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high-dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA-SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm −1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.

show abstract

“…Identifies types of gastric diseases from blood serum [69] Accelerometer, gyroscope LSTM b) Classification Predicting the severity of Parkinsonian tremors in free body movement [70] Accelerometer, gyroscope RF i) Classification Real-time detection and classification of FoG events [71] MO x gas sensor b) PCA e) , RF i) PA&FS j) Classification Regression…”

Section: Classificationmentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

View full text Add to dashboard Cite

Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

show abstract

Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases

Cited by 53 publications

References 33 publications

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

Machine Learning‐Enabled Smart Sensor Systems

Contact Info

Product

Resources

About