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Application of the genetic risk model for the analysis of predisposition to nonlacunar ischemic stroke.

Vitaly KorchaginKonstantin MironovAlexander PlatonovOlga DribnokhodovaElina AkselrodElena DunaevaMarina StolyarAleksey GorbenkoGalina GritsanIgor OlkhovskiyNatalia RakovaAlexander RoytmanAlexander SotnikovAnton RaskurazhevMarina MaksimovaMarine TanashyanSergey IllarioshkinGerman Shipulin
Published in: Personalized medicine (2019)
Aim: The purpose of our study was to analyze the predictive ability of the multiplicative model of genetic risk of nonlacunar ischemic stroke (IS) for independent samples from Russia. Patients & methods: A total of 181 patients and 360 healthy controls were included in this study. The discriminative accuracy of model was evaluated by the area under the receiver operating characteristic curve (AUC). Results: Classification model based on 15 single-nucleotide polymorphisms (SNPs), which are associated with a cardioembolic subtype of IS, had an AUC of 0.62 in patients with corresponding subtypes and an AUC of 0.58 for all patients. Conclusion: Risk calculation approach based on IS-associated SNPs had satisfactory performance in predicting the predisposition to the disease.
Keyphrases
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  • machine learning
  • gene expression
  • atrial fibrillation
  • deep learning
  • dna methylation