Evidence map›Paper›PMID 41343129›Full record

ArticleJournal of molecular neuroscience : MN2025

An Improved Deep Semi-supervised JNMF Method for Biomarker Extraction of Alzheimer's Disease.

Yawen Chen, Wei Kong, Kun Liu, Kai Wei, Gen Wen, Yaling Yu, Yuemin Zhu

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In one paragraph

Article in Journal of molecular neuroscience : MN, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yawen Chen *College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, P. R. China.
Wei Kong *College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, P. R. China. weikong@shmtu.edu.cn.
Kun LiuCollege of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, P. R. China.
Kai WeiBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Gen WenDepartment of Orthopedic Surgery, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
Yaling YuDepartment of Orthopedic Surgery, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
Yuemin ZhuINSA Lyon, Universite Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69621, Lyon, France.

Funding

Natural Science Foundation of Shanghai Municipality No.18ZR1417200
6 · The paper itself

Abstract

Imaging genetics is an approach that explores the underlying mechanisms of brain disorders such as Alzheimer's disease (AD) by analyzing the correlation between neuroimaging and genetic data. Traditional non-negative matrix factorization (NMF) algorithms are based on linear assumptions, which limits the potential of nonlinear feature extraction among multi-omics data. This study proposes a novel joint-connectivity-based deep semi-supervised non-negative matrix factorization (JCB-DSNMF) model to overcome this limitation and incorporate prior knowledge from both within and between different modalities of data. The model effectively integrates physiological constraints such as connectivity to identify regions of interest (ROI), risk genes, and risk SNP loci associated with AD patients. JCB-DSNMF outperformed other NMF-based algorithms, such as JDSNMF and NMF, in identifying and predicting biologically relevant biomarkers closely related to AD from essential modules. The accuracy of the selected features was further validated by constructing a diagnostic model with high classification accuracy, achieving an AUC value of 0.8621 on the test set. In particular, the brain region Putamen_L and the gene RALGAPB achieved AUC values of 0.903 and 0.924, respectively, highlighting the importance of these features in early AD diagnosis.

Indexed as

Alzheimer DiseaseAlgorithmsBiomarkersBrainFemaleHumansMalePolymorphism, Single NucleotideBiomarkersAlzheimer’s diseaseBiomarkersBrain imaging geneticsDeep learningMild cognitive impairmentNonnegative matrix factorization

Identifiers

PMID41343129

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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.