Novel classification for global gene signature model for predicting severity of systemic sclerosis.

Autor:	Zariel I Johnson, Jacqueline D Jones, Angana Mukherjee, Dianxu Ren, Carol Feghali-Bostwick, Yvette P Conley, Cecelia C Yates
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Medicine Science
Zdroj:	PLoS ONE, Vol 13, Iss 6, p e0199314 (2018)
Druh dokumentu:	article
ISSN:	1932-6203
DOI:	10.1371/journal.pone.0199314
Popis:	Progression of systemic scleroderma (SSc), a chronic connective tissue disease that causes a fibrotic phenotype, is highly heterogeneous amongst patients and difficult to accurately diagnose. To meet this clinical need, we developed a novel three-layer classification model, which analyses gene expression profiles from SSc skin biopsies to diagnose SSc severity. Two SSc skin biopsy microarray datasets were obtained from Gene Expression Omnibus. The skin scores obtained from the original papers were used to further categorize the data into subgroups of low (
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/1d03c46965544a7b9d868d0e1e7252c0 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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