Discovery of stroke-related blood biomarkers from gene expression network models.

ABSTRACT:

Background

Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body's response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke.

Methods

Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene's significance in the

SUBMITTER: Theofilatos K 

PROVIDER: S-EPMC6686563 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature