Integrating hypertension phenotype and genotype with hybrid non-negative matrix factorization
Author(s) -
Yuan Luo,
Chengsheng Mao,
Yiben Yang,
Fei Wang,
Faraz S. Ahmad,
Donna K. Arnett,
Marguerite R. Irvin,
Sanjiv J. Shah
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty804
Subject(s) - non negative matrix factorization , phenotype , subtyping , genotype , matrix decomposition , computer science , bayesian probability , computational biology , artificial intelligence , data mining , biology , genetics , gene , eigenvalues and eigenvectors , quantum mechanics , programming language , physics
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. 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.
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