Evidence map›Paper›PMID 40224009›Full record

ArticleiScience2025

Distinguishing different proteins based on terahertz spectra by visual geometry group 16 neural network.

Yusa Chen, Xiwen Huang, Meizhang Wu, Jixuan Hao, Yunhao Cao, Hongshun Sun, Lijun Ma, Liye Li, Wengang Wu, Guozhong Zhao and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yusa ChenNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Beijing 100871, P.R. China.
Xiwen HuangDepartment of Physics, Capital Normal University, Beijing 100048, China.
Meizhang WuSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100096, China.
Jixuan HaoDepartment of Physics, Capital Normal University, Beijing 100048, China.
Yunhao CaoNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Beijing 100871, P.R. China.
Hongshun SunNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Beijing 100871, P.R. China.
Lijun MaNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Beijing 100871, P.R. China.
Liye LiNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Beijing 100871, P.R. China.
Wengang WuNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Beijing 100871, P.R. China.
Guozhong ZhaoDepartment of Physics, Capital Normal University, Beijing 100048, China.
Tianhua MengInstitute of Solid State Physics, Shanxi Provincial Key Laboratory of Microstructure Electromagnetic Functional Materials, Shanxi Datong University, Datong 037009, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting different kinds of proteins is of great significance for medical diagnosis, biological research, and other fields. We combine both terahertz (THz) absorption and refractive index spectra with the visual geometry group 16 (VGG-16) neural network to intelligently identify four proteins, namely albumin, collagen, pepsin, and pancreatin in this study. The THz absorption-refractive index spectra of the proteins were converted to two-dimensional image features by the Grassia angular summation field (GASF) method and used as a dataset, which enabled the VGG-16 model to achieve 98.8% accuracy in distinguishing the four proteins. We also compared the VGG-16 model with other machine learning models, which demonstrate that it has better performance. Overall, the VGG-16 neural network transfer learning technique proposed in this study can quickly and accurately achieve the identification of different kinds of proteins. This research might have potentially important applications in biotechnology fields, such as biosensors, biopharmaceuticals, and medicine.

Indexed as

Applied computing in medical scienceArtificial intelligence applicationsArtificial intelligence programming language

Identifiers

PMID40224009
PMCPMC11987644

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.