PhenoEncoder: A Discriminative Embedding Approach to Genomic Data Compression

Exploring the heritability of complex genetic traits requires methods that can handle the genome's vast scale and the intricate relationships among genetic markers. Widely accepted association studies overlook non-linear effects (epistasis), prompting the adoption of deep neural networks (DNNs)...

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Vydané v:	bioRxiv
Hlavní autori:	Tas, Gizem, Postma, Eric, Balvert, Marleen, Schoenhuth, Alexander
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Cold Spring Harbor Cold Spring Harbor Laboratory Press 10.12.2024 Cold Spring Harbor Laboratory
Vydanie:	1.1
Predmet:	Compression Data compression Datasets Embedding Epistasis Genetic diversity Genetic markers Genomes Genomics Genotype & phenotype Haplotypes Heritability Linkage disequilibrium Neural networks Phenotypes Phenotypic variations Single-nucleotide polymorphism Autoencoder Discriminative Embedding Genomic Feature Extraction
ISSN:	2692-8205, 2692-8205
On-line prístup:	Získať plný text
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