Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

ABSTRACT: Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predict

ORGANISM(S): Homo sapiens

SUBMITTER: Michael Gurevich 

PROVIDER: E-GEOD-15245 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress