@article{b7a9d0a0623b4b229640c712da9c5faf,
title = "Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach",
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.",
keywords = "gene regulatory network, machine learning, psoriasis, regulators, transcriptome",
author = "Jingwen Deng and Carlotta Schieler and Borghans, {Jos{\'e} A.M.} and Chuanjian Lu and Aridaman Pandit",
note = "Funding Information: We appreciated the bio-sample donations of participants of validation cohort in the study. We would like to express our gratitude to the contributors of GEO transcriptome datasets (GSE67785, GSE83645, GSE63979, GSE65832, GSE140227 and GSE121212). Funding Information: JD was supported by the China Scholarship Council (CSC) NO. 202007720051; National Natural Science Foundation of China (U20A20397) and Science and Technology Planning Project of Guangdong Province (2020B1111100005). Publisher Copyright: Copyright {\textcopyright} 2022 Deng, Schieler, Borghans, Lu and Pandit.",
year = "2022",
month = jul,
day = "7",
doi = "10.3389/fimmu.2022.921408",
language = "English",
volume = "13",
journal = "Frontiers in Immunology",
issn = "1664-3224",
publisher = "Frontiers Media S. A.",
}