Evidence map›Paper›PMID 40466864›Full record

ArticleMolecular & cellular proteomics : MCP2025

PTMFusionNet: A Deep Learning Approach for Predicting Disease Related Post-translational Modification and Classifying Disease Subtypes.

Jie Ni, Yifan Zhou, Bin Li, Xinting Zhang, Yuanyuan Deng, Jie Sun, Donghui Yan, Shengqi Jing, Shan Lu, Zhuoying Xie and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Jie NiInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China.
Yifan ZhouInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China.
Bin LiInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China.
Xinting ZhangInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China.
Yuanyuan DengInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China.
Jie SunInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China.
Donghui YanInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China.
Shengqi JingDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China; Department of Information, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China; Center for Data Management, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Shan LuWomen and Children Department, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Zhuoying XieInstitute for Molecular Medical Technology, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, Jiangsu, China. Electronic address: zyxie@seu.edu.cn.
Xin ZhangDepartment of Information, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address: zx6800@njmu.edu.cn.
Yun LiuDepartment of Information, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address: liuyun@njmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advancement of technologies such as mass spectrometry, it has become possible to simultaneously perform large-scale detection of protein intensity and corresponding post-translational modification (PTM) information, thereby facilitating clinical diagnosis and treatment. However, existing PTM information is insufficient to fully integrate with protein expression data. We propose a deep learning method called PTMFusionNet, which predicts potential disease-related PTMs and integrates them with protein expression data to classify disease subtypes. PTMFusionNet includes two Graph Convolutional Network (GCN) models: the Layer-Attention Graph Convolutional Network (LAGCN) and the Feature Weighting Graph Convolutional Network (FWGCN). LAGCN is used to predict PTM potentiality scores, while FWGCN integrates these scores with protein expression data for disease subtype classification. Experimental results across three datasets (KIPAN, COADREAD, and THCA) demonstrate that PTMFusionNet outperforms benchmark algorithms in accuracy, F1 score, and AUC, highlighting its robustness in identifying critical PTM biomarkers and advancing disease subtyping.

Indexed as

Computational BiologyDeep LearningProtein Processing, Post-TranslationalAlgorithmsHumansbiomarker discoverydisease classificationfeature weightinggraph convolutional networkpost-translational modification

Identifiers

PMID40466864
PMCPMC12365514

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.