ArticleFrontiers in immunology2022
Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
The trial behind it
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Integrative Transcriptomic and Genetic Analyses Identify CDC20 as a Potential Biomarker Associated with Cell Cycle and Immune-Inflammatory Processes in Psoriasis.Clinical, cosmetic and investigational dermatology · 2026Article
- Identification and verification of three autophagy-related genes as potential biomarkers for the diagnosis of psoriasis.Scientific reports · 2023Article
- Sericin coated thin polymeric films reduce keratinocyte proliferation via the mTOR pathway and epidermal inflammation through IL17 signaling in psoriasis rat model.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psoriasis is a chronic inflammatory skin disorder. Although it has been studied extensively, the molecular mechanisms driving the disease remain unclear. In this study, we utilized a tree-based machine learning approach to explore the gene regulatory networks underlying psoriasis. We then validated the regulators and their networks in an independent cohort. We identified some key regulators of psoriasis, which are candidates to serve as potential drug targets and disease severity biomarkers. According to the gene regulatory network that we identified, we suggest that interferon signaling represents a key pathway of psoriatic inflammation.
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Registered trials
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