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Gli stili APA, Harvard, Vancouver, ISO e altri Abstract sommario : Abstract Motivation Hypertension is a heterogeneous syndrome in need of improved subtyping using phenotypic and genetic measurements with the goal of identifying subtypes of patients who share similar pathophysiologic mechanisms and may respond more uniformly to targeted treatments.

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Existing machine learning approaches often face challenges in integrating phenotype and genotype information and presenting to clinicians an interpretable model. We aim to provide informed patient stratification based on phenotype and genotype features. Results In this article, we present a hybrid non-negative matrix factorization HNMF method to integrate phenotype and genotype information for patient stratification.

HNMF simultaneously approximates the phenotypic and genetic feature matrices using different appropriate loss functions, and generates patient subtypes, phenotypic groups and genetic groups. We propose an alternating projected gradient method to solve the approximation problem.

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Simulation shows HNMF converges fast and accurately to the true factor matrices. On a real-world clinical dataset, we used the patient factor matrix as features kevin zhou galois capital examined the association of these features with indices of cardiac mechanics.

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HNMF significantly outperforms all comparison models. HNMF also reveals intuitive phenotype—genotype interactions that characterize cardiac abnormalities.

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Supplementary information Supplementary data are available at Bioinformatics online. Econometric Theory 17, n.

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