Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.

ABSTRACT: Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, whe

SUBMITTER: O'Connell GC 

PROVIDER: S-EPMC5685316 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature