Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population

Abstract

Abstract:Purpose:Our group recently identified a ten gene pattern of differential expression in peripheral blood which may have utility for detection of stroke. The objective of this study was to assess the diagnostic capacity and temporal stability of this stroke-associated transcriptional signature in an independent patient population. Methods:Publically available whole blood microarray data generated from 23 ischemic stroke patients at 3, 5, and 24 hours post-symptom onset, along with 23 cardiovascular disease controls were obtained via the National Center for Biotechnology Information Gene Expression Omnibus. Expression levels of the ten candidate genes were extracted, compared between groups, and evaluated for their discriminatory ability at each time point. Results:We observed a largely identical pattern of differential expression between stroke patients and controls across the ten candidate genes as reported in our prior work. Furthermore, the coordinate expression levels of the ten candidate genes were able to discriminate between stroke patients and controls with levels of sensitivity and specificity upwards of 90% across all three time points. Conclusions:These findings confirm the diagnostic robustness of the previously identified pattern of differential expression in independent patient population, and further suggest that it is temporally stable over the first 24 hours of stroke pathology.

Machine Learning for Brain Stroke: A Review

“…Additionally, [51] proposed a fully automatic strategy by identifying the Intima-Media Thickness boundaries from ultrasound common carotid artery images using Artificial Neural Networks (ANN) and training computational time is 1.4s. Genome-wide transcriptional profiling can be useful in stroke detection.Study [56] identified a 10-gene pattern of differential expression using ML technique (here, genetic algorithm/kNN) which has enough ability for stroke detection.…”