Discovery of stroke-related blood biomarkers from gene expression network models
| Autor: | Seferina Mavroudi, Matthew C. Cowperthwaite, Max Shpak, Aigli Korfiati, Konstantinos Theofilatos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
lcsh:Internal medicine lcsh:QH426-470 Gene regulatory network Computational biology Biology Brain Ischemia 03 medical and health sciences 0302 clinical medicine Gene expression microRNA Databases Genetic Genetics Humans Gene Regulatory Networks lcsh:RC31-1245 Gene Genetics (clinical) Models Genetic Gene Expression Profiling Gene networks Molecular Sequence Annotation Gene signature Human genetics Stroke lcsh:Genetics MicroRNAs 030104 developmental biology Gene Expression Regulation 030220 oncology & carcinogenesis DNA microarray Centrality Biomarkers Research Article |
| Zdroj: | BMC Medical Genomics BMC Medical Genomics, Vol 12, Iss 1, Pp 1-15 (2019) |
| ISSN: | 1755-8794 |
| Popis: | 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 network was assessed according to the differences in their network’s pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm – support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model. Results A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes (ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies |
| Databáze: | OpenAIRE |
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