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Article

Autoencoders for genomic variation analysis.

Genome research - 3 Feb 2026

Geleta Margarita, Montserrat Daniel Mas, Giro-I-Nieto Xavier, Ioannidis Alexander G

Abstract excerpt

Modern biobanks are providing numerous high-resolution genomic sequences of diverse populations. In order to account for diverse and admixed populations, new algorithmic tools are needed in order to properly capture the genetic composition of populations. Here, we explore deep learning techniques, namely, variational autoencoders (VAEs), to process genomic data from a population perspective. We show the power of...

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Algorithms
Deep Learning
Genetic Variation
Genome, Human
Genomics
Polymorphism, Single Nucleotide

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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PMID: 41558827
DOI: 10.1101/gr.280086.124
PMCID: PMC12863191

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 31 Aug 2026.

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