Evidence map›Paper›PMID 39651564›Full record

ArticleCurrent computer-aided drug design2025

DeepTransformer: Node Classification Research of a Deep Graph Network on an Osteoporosis Graph based on GraphTransformer.

Yixin Liu, Guowei Jiang, Miaomiao Sun, Ziyan Zhou, Pengchen Liang, Qing Chang

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Article in Current computer-aided drug design, 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

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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. 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

6 authors.

Yixin LiuSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Guowei JiangPharmacy Department, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
Miaomiao SunShanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
Ziyan ZhouShanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
Pengchen LiangSchool of Microelectronics, Shanghai University, Shanghai, 201800, China.
Qing ChangSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Funding

Combination of Medical Care and Health Project of Shanghai University of Traditional Chinese Medicine YYKC-2021-01-020Key Projects of Jiading District Health Commission, Shanghai 2020-ZD-03Scientific Fund of Shanghai University of Medicine and Health Sciences SSF-22-16-003Shanghai Key Specialty of Clinical Pharmacy YXZDZK-01
6 · The paper itself

Abstract

backgroundOsteoporosis (OP) is one of the most common diseases in the elderly population. It is mostly treated with medication, but drug research and development have the disadvantage of taking a long time and having a high cost.

objectiveTherefore, we developed a graph neural network with the help of artificial intelligence to provide new ideas for drug research and development for OP.

methodsIn this study, we built a new osteoporosis graph (called OPGraph) and proposed a deep graph neural network (called DeepTransformer) to predict new drugs for OP. OPGraph is a graph data model established by gathering features and their interrelationships from a vast amount of OP data. DeepTransformer uses GraphTransformer as its foundational network and applies residual connections for deep layering.

resultsThe analysis and results showed that DeepTransformer outperformed numerous models on OPGraph, with area under the curve (AUC) and area under the precision-recall curve (AUPR) reaching 0.9916 and 0.9911, respectively. In addition, we conducted an

conclusionThe model we developed with the help of artificial intelligence can effectively reduce the time and cost of OP drug development and reduce the heavy economic burden brought to patient's family by complications caused by osteoporosis.

Indexed as

Neural Networks, ComputerOsteoporosisArtificial IntelligenceDeep LearningHumansartificial intelligencedeep graph neural networkdrug-target interactionGraphTransformermedication.Osteoporosis

Identifiers

PMID39651564

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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.