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在银屑病中寻找基因调控网络:基于树的机器学习方法的应用。

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

机构信息

Center for Translational Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.

The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.

出版信息

Front Immunol. 2022 Jul 7;13:921408. doi: 10.3389/fimmu.2022.921408. eCollection 2022.

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.

摘要

银屑病是一种慢性炎症性皮肤病。尽管已经进行了广泛的研究,但导致这种疾病的分子机制仍不清楚。在这项研究中,我们利用基于树的机器学习方法来探索银屑病的基因调控网络。然后,我们在一个独立的队列中验证了这些调节剂及其网络。我们确定了一些银屑病的关键调节剂,它们是潜在的药物靶点和疾病严重程度生物标志物的候选物。根据我们确定的基因调控网络,我们提出干扰素信号代表了银屑病炎症的关键途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb62/9301015/72e51ed0487c/fimmu-13-921408-g001.jpg

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