DengMCEisenHJMehraMR. Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am J Transplant2006; 6: 150–160.
2.
Van de VijverMJHeYDVan’t VeerLJ. A gene-expression signature as a predictor of survival in breast cancer. N Eng J Med2002; 347: 1999–2009.
3.
RamanathanMWeinstock-GuttmanBNguyenLT. In vivo gene expression revealed by cDNA arrays: The pattern in relapsing–remitting multiple sclerosis patients compared with normal subjects. J Neuroimmunol2001; 116: 213–219.
4.
LockCHermansGPedottiR. Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat Med2002; 8: 500–508.
5.
WhitneyLWBeckerKGTresserNJ. Analysis of gene expression in mutiple sclerosis lesions using cDNA microarrays. Ann Neurol1999; 46: 425–428.
6.
SatohJNanriYTabunokiH. Microarray analysis identifies a set of CXCR3 and CCR2 ligand chemokines as early IFNbeta-responsive genes in peripheral blood lymphocytes in vitro: An implication for IFNbeta-related adverse effects in multiple sclerosis. BMC Neurol2006; 6: 18.
7.
ArthurATArmatiPJByeC. Genes implicated in multiple sclerosis pathogenesis from consilience of genotyping and expression profiles in relapse and remission. BMC Med Genet2008; 9: 17.
8.
GurevichMTullerTRubinsteinU. Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells. BMC Med Genom2009; 2: 46.
9.
CorvolJCPelletierDHenryRG. Abrogation of T cell quiescence characterizes patients at high risk for multiple sclerosis after the initial neurological event. Proc Natl Acad Sci USA2008; 105: 11839–11844.
10.
BaranziniSEMousaviPRioJ. Transcription-based prediction of response to IFNbeta using supervised computational methods. PLoS Biol2005; 3: e2.
11.
RudickRARaniMRXuY. Excessive biologic response to IFNbeta is associated with poor treatment response in patients with multiple sclerosis. PLoS ONE2011; 6: e19262.