Evidence map›Paper›PMID 40797207›Full record

ArticleBMC biology2025

DualNetM: an adaptive dual network framework for inferring functional-oriented markers.

Bingjie Dai, Hanshuang Li, Peizhuo Wang, Pengwei Hu, Jixiang Xing, Yanan Hu, Qilemuge Xi, Yongchun Zuo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

8 authors.

Bingjie Dai *State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China.
Hanshuang Li *State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China.
Peizhuo Wang *School of Life Science and Technology, Xidian University, Shaanxi, Xi'an, 710071, China.
Pengwei HuState Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China.
Jixiang XingState Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China.
Yanan HuState Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China.
Qilemuge XiState Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China.
Yongchun ZuoState Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China. yczuo@imu.edu.cn.

Funding

the China Postdoctoral Science Foundation 2024MD763987the National Nature Scientific Foundation of China 62061034, 62171241, 62461046
6 · The paper itself

Abstract

backgroundUnderstanding how genes regulate each other in cells is crucial for determining cell identity and development, and single-cell sequencing technologies facilitate such research through gene regulatory networks (GRNs). However, identifying important marker genes within these complex networks remains difficult.

resultsConsequently, we present DualNetM, a deep generative model with a dual-network framework for inferring functional-oriented markers. It employs graph neural networks with adaptive attention mechanisms to construct GRNs from single-cell data. Functional-oriented markers are identified from bidirectional co-regulatory networks through the integration of gene co-expression networks. Benchmark tests highlighted the superior performance of DualNetM in constructing GRNs, along with a stronger association with biological functions in marker inference. In the melanoma dataset, DualNetM successfully inferred novel malignant markers, and survival analysis results showed that multiple novel markers were associated with lethality in malignant melanoma. Additionally, DualNetM identified stage-specific functional markers and clarified their specific roles in mouse embryonic fibroblast reprogramming. DualNetM's marker inference function demonstrated stronger biological relevance during primed reprogramming.

conclusionsIn summary, DualNetM effectively facilitated the inference of functional-oriented markers from complex GRNs.

Indexed as

BiomarkersGene Regulatory NetworksMelanomaNeural Networks, ComputerAnimalsGenetic MarkersHumansMiceSingle-Cell AnalysisBiomarkersGenetic MarkersDual-network frameworkFunctional-oriented markersGene regulatory networkSingle-cell data

Identifiers

PMID40797207
PMCPMC12345081

What OpenQuestion 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 OpenQuestion graph.