Evidence map›Paper›PMID 42880922›Full record

ArticleBeijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences2026

[Explore the potential diagnostic markers of primary Sjögren syndrome by bioinformatics combined with machine learning].

Bingle Li, Li Bai, Yongfu Wang, Lingyan Zhu

Abstract readEnglish Abstract
PubMed Full text at PMC
In one paragraph

Article in Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences, 2026. 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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1 · What the graph read from it

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2 · The registry

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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

4 authors.

Bingle LiBaotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongalia, China.
Li BaiThe Central Laboratory, the First Affiliated Hospital of Baotou Medical College, Baotou 014010, Inner Mongalia, China.
Yongfu WangDepartment of Rheumatology and Immunology, the First Affiliated Hospital of Baotou Medical College, Baotou 014010, Inner Mongalia, China.
Lingyan ZhuDepartment of Rheumatology and Immunology, the First Affiliated Hospital of Baotou Medical College, Baotou 014010, Inner Mongalia, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a diagnostic model for primary Sjögren syndrome (pSS) utilizing transcriptomics-based machine learning techniques, and to explore potential therapeutic targets.

methodsWe selected patients diagnosed with pSS and control subjects with salivary gland samples from the Gene Expression Omnibus (GEO) database for transcriptome sequencing. This was complemented by methylation sequencing data, followed by weighted gene co-expression network analysis (WGCNA) in conjunction with clinical data to identify candidate diagnostic genes. Subsequently, we refined the gene list using least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian mixed model (GMM), and support vector machine (SVM). Ultimately, we constructed a composite diagnostic model for pSS employing SVM, neural networks (NN), Logistic regression (LR), decision trees (DT), adaptive boosting (AdaBoost), bootstrap aggregating (Bagging), random forest, and Naive Bayes classifiers, totaling eight distinct machine learning algorithms, and predicted drug targets.

resultsThrough differential gene screening combined with WGCNA analysis of the GEO database, we identified 12 key genes. Further variable selection

conclusionThis research identifies

Indexed as

Computational BiologyMachine LearningSjogren's SyndromeBayes TheoremBiomarkersClassification AlgorithmsGene Expression ProfilingHumansPredictive Learning ModelsRandom ForestSupport Vector MachineBiomarkersBioinformaticsBiomarkersDiagnostic modelMachine learningPrimary Sjögren syndrome

Identifiers

PMID42880922
PMCPMC13672237

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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.