ArticleInternational journal of molecular sciences2026
Integrated Transcriptomic and Machine Learning Analysis Reveals Immune-Related Regulatory Networks in Anti-NMDAR Encephalitis.
Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Multi-omics integration in autoimmune encephalitis: from novel biomarker discovery to precision therapeutic strategies.Frontiers in immunology · 2026Review
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Authors and funding
3 authors.
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Abstract
Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an immune-mediated neurological disorder driven by dysregulated neuroimmune interactions, yet the molecular architecture linking tumor-associated immune activation, peripheral immunity, and neuronal dysfunction remains insufficiently understood. In this study, we established an integrative computational framework that combines multi-tissue transcriptomic profiling, weighted gene co-expression network analysis, immune deconvolution, and machine learning-based feature prioritization to systematically characterize the regulatory landscape of the disease. Joint analysis of three independent GEO datasets spanning ovarian teratoma tissue and peripheral blood transcriptomes identified 2001 consistently dysregulated mRNAs, defining a shared tumor-immune-neural transcriptional axis. Across multiple feature selection algorithms,
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