Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học General Psychiatry cung cấp cho các bạn kiến thức về ngành y đề tài: Novel approaches to gene expression analysis of active polyarticular juvenile rheumatoid arthritis. | Available online http content 6 1 R15 Research article Open Access Novel approaches to gene expression analysis of active polyarticular juvenile rheumatoid arthritis James N Jarvis 1 Igor Dozmorov 2 Kaiyu Jiang1 Mark Barton Frank2 Peter Szodoray3 Philip Alex2 and Michael Centola2 1 Department of Pediatrics University of Oklahoma College of Medicine Oklahoma City OK USA 2Department of Arthritis and Immunology Oklahoma Medical Research Foundation Oklahoma City OK USA 3Broegelmann Research Laboratory The Gade Institute University of Bergen Bergen Norway Drs Jarvis and Dozmorov contributed equally to this work. Correspondence James N Jarvis james-jarvis@ Received 30 May 2003 Revisions requested 27 Jul 2003 Revisions received 5 Sep 2003 Accepted 2 Oct 2003 Published 6 Nov 2003 Arthritis Res Ther 2004 6 R15-R32 DOI ar1018 2004 Jarvis et al. licensee BioMed Central Ltd Print ISSN 1478-6354 Online ISSN 1478-6362 . This is an Open Access article verbatim copying and redistribution of this article are permitted in all media for any purpose provided this notice is preserved along with the article s original URL. Abstract Juvenile rheumatoid arthritis JRA has a complex poorly characterized pathophysiology. Modeling of transcriptosome behavior in pathologic specimens using microarrays allows molecular dissection of complex autoimmune diseases. However conventional analyses rely on identifying statistically significant differences in gene expression distributions between patients and controls. Since the principal aspects of disease pathophysiology vary significantly among patients these analyses are biased. Genes with highly variable expression those most likely to regulate and affect pathologic processes are excluded from selection as their distribution among healthy and affected individuals may overlap significantly. Here we describe a novel method for analyzing microarray data that assesses statistically significant changes in gene .
