Evidence map›Paper›PMID 39679296›Full record

ArticleiScience2024

ProteoNet: A CNN-based framework for analyzing proteomics MS-RGB images.

Jinze Huang, Yimin Li, Bo Meng, Yong Zhang, Yaoguang Wei, Xinhua Dai, Dong An, Yang Zhao, Xiang Fang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
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

9 authors.

Jinze HuangTechnology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China.
Yimin LiCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Bo MengTechnology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China.
Yong ZhangInstitutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu 610041, China.
Yaoguang WeiCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Xinhua DaiTechnology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China.
Dong AnCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Yang ZhaoTechnology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China.
Xiang FangTechnology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteomics is crucial in clinical research, yet the clinical application of proteomic data remains challenging. Transforming proteomic mass spectrometry (MS) data into red, green, and blue color (MS-RGB) image formats and applying deep learning (DL) techniques has shown great potential to enhance analysis efficiency. However, current DL models often fail to extract subtle, crucial features from MS-RGB data. To address this, we developed ProteoNet, a deep learning framework that refines MS-RGB data analysis. ProteoNet incorporates semantic partitioning, adaptive average pooling, and weighted factors into the Convolutional Neural Network (CNN) model, thus enhancing data analysis accuracy. Our experiments with proteomics data from urine, blood, and tissue samples related to liver, kidney, and thyroid diseases demonstrate that ProteoNet outperforms existing models in accuracy. ProteoNet also provides a direct conversion method for MS-RGB data, enabling a seamless workflow. Moreover, its compatibility with various CNN architectures, including lightweight models like MobileNetV2, underscores its scalability and clinical potential.

Indexed as

Computer-aided diagnosis methodMachine learningProteomics

Identifiers

PMID39679296
PMCPMC11638609

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.