[HTML][HTML] Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
M Elgart, G Lyons, S Romero-Brufau… - Communications …, 2022 - nature.com
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
M Elgart, G Lyons, S Romero-Brufau… - Communications …, 2022 - pubmed.ncbi.nlm.nih.gov
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.
M Elgart, G Lyons, S Romero-Brufau… - Communications …, 2022 - search.ebscohost.com
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
[HTML][HTML] Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
M Elgart, G Lyons, S Romero-Brufau… - Communications …, 2022 - ncbi.nlm.nih.gov
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
[PDF][PDF] Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
M Elgart, G Lyons, S Romero-Brufau, N Kurniansyah… - academia.edu
Results We used a multi-ethnic dataset from TOPMed containing 29,063 genotyped
individuals from eight distinct cohorts (JHS, FHS, HCHS/SOL, ARIC, CHS, MESA, CFS, and …
individuals from eight distinct cohorts (JHS, FHS, HCHS/SOL, ARIC, CHS, MESA, CFS, and …
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.
M Elgart, G Lyons, S Romero-Brufau… - Communications …, 2022 - europepmc.org
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
M Elgart, G Lyons, S Romero-Brufau… - Communications …, 2022 - search.proquest.com
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …
trait, yet they fail to account for non-linear and interaction effects between single nucleotide …