Haplotype-based autoencoders can reduce the dataset dimension and estimate haplotype block effects in different crop species

Abstract Background In plant breeding, many studies currently investigate the application of machine learning (ML) to genomic prediction, hoping for an improvement in prediction accuracy compared to standard models like Genomic Best Linear Unbiased Prediction (GBLUP). However, ML algorithms require...

Volledige beschrijving

Bibliografische gegevens

Hoofdauteurs:	Philipp Georg Heilmann, Emanuel Grosch, Matthias Frisch, Matthias Herrmann, Steffen Beuch, Vivek Kurra, Martin Mascher, Raz Avni, Klaus Oldach, Ina Röhrs, Anja Hanemann, Raja Ram Mehta, Carsten Reinbrecht, Albrecht Serfling, Andreas Stahl, Marco Stucke, Amine Abbadi, Tobias Kox, Carola Zenke-Philippi
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	BMC 2025-12-01
Reeks:	BMC Bioinformatics
Onderwerpen:	Plant breeding Machine learning Genomic prediction Neural networks Autoencoders Dimensionality reduction
Online toegang:	https://doi.org/10.1186/s12859-025-06323-w
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!