Fast identification and quantification of c-Fos protein using you-only-look-once-v5
Bibliographic Data
| ID | 15527906 |
|---|---|
| Authors | Na Pang (0000-0002-5434-3282, Northeastern University), Zihao Liu (0009-0000-4946-1746, Guangzhou University of Chinese Medicine), Zhengrong Lin (0000-0001-5600-0710, Chinese Academy of Sciences), Xiaoyan Chen (0009-0001-2179-4968, Chinese Academy of Sciences), Xiufang Liu (0009-0009-5148-5013, Shenzhen Institutes of Advanced Technology, a), Min Pan (0000-0001-9276-5163, Guangzhou University of Chinese Medicine), Keke Shi (0009-0003-8971-9299, Shenzhen Institutes of Advanced Technology, a, corresponding author), Yang Xiao (0000-0001-9237-8913, corresponding author), Lisheng Xu (0000-0001-8360-3605, Northeastern University, corresponding author) |
| Year | 2022 |
| Volume | 13 |
| Pages | 1011296-1011296 |
| Publication date | 2022-09-23 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2022.1011296 |
| PMID | 36213931 |
| OpenAlex | W4296830330 |
| Language | EN |
| References cited | 43 |
In neuroscience, protein activity characterizes neuronal excitability in response to a diverse array of external stimuli and represents the cell state throughout the development of brain diseases. Importantly, it is necessary to characterize the proteins involved in disease progression, nuclear function determination, stimulation method effect, and other aspects. Therefore, the quantification of protein activity is indispensable in neuroscience. Currently, ImageJ software and manual counting are two of the most commonly used methods to quantify proteins. To improve the efficiency of quantitative protein statistics, the you-only-look-once-v5 (YOLOv5) model was proposed. In this study, c-Fos immunofluorescence images data set as an example to verify the efficacy of the system using protein quantitative statistics. The results indicate that YOLOv5 was less time-consuming or obtained higher accuracy than other methods (time: ImageJ software: 80.12 ± 1.67 s, manual counting: 3.41 ± 0.25 s, YOLOv5: 0.0251 ± 0.0003 s, p n = 83; simple linear regression equation: ImageJ software: Y = 1.013 × X + 0.776, R 2 = 0.837; manual counting: Y = 1.0* X + 0, R 2 = 1; YOLOv5: Y = 0.9730* X + 0.3821, R 2 = 0.933, n = 130). The findings suggest that the YOLOv5 algorithm provides feasible methods for quantitative statistical analysis of proteins and has good potential for application in detecting target proteins in neuroscience
Biology · Botany · Chromatography · Computational biology · Identification (biology · Advanced Proteomics Techniques and Applications · Chemistry · Glycosylation and Glycoproteins Research · Machine Learning in Bioinformatics
| Highly cited | No |
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