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Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach

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...

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Autores principales: Deng, Jingwen, Schieler, Carlotta, Borghans, José A. M., Lu, Chuanjian, Pandit, Aridaman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9301015/
https://www.ncbi.nlm.nih.gov/pubmed/35874668
http://dx.doi.org/10.3389/fimmu.2022.921408
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author Deng, Jingwen
Schieler, Carlotta
Borghans, José A. M.
Lu, Chuanjian
Pandit, Aridaman
author_facet Deng, Jingwen
Schieler, Carlotta
Borghans, José A. M.
Lu, Chuanjian
Pandit, Aridaman
author_sort Deng, Jingwen
collection PubMed
description 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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spelling pubmed-93010152022-07-22 Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach Deng, Jingwen Schieler, Carlotta Borghans, José A. M. Lu, Chuanjian Pandit, Aridaman Front Immunol Immunology 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. Frontiers Media S.A. 2022-07-07 /pmc/articles/PMC9301015/ /pubmed/35874668 http://dx.doi.org/10.3389/fimmu.2022.921408 Text en Copyright © 2022 Deng, Schieler, Borghans, Lu and Pandit https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Deng, Jingwen
Schieler, Carlotta
Borghans, José A. M.
Lu, Chuanjian
Pandit, Aridaman
Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach
title Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach
title_full Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach
title_fullStr Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach
title_full_unstemmed Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach
title_short Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach
title_sort finding gene regulatory networks in psoriasis: application of a tree-based machine learning approach
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9301015/
https://www.ncbi.nlm.nih.gov/pubmed/35874668
http://dx.doi.org/10.3389/fimmu.2022.921408
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