Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.

Plant communications - 11 May 2026

Wang Xin, Jiang Zhenting, Ding Tongtong, Cao Ying, Zhou Kai, Yu Guangning, Li Pengcheng, Yang Zefeng, Zhang Xuecai, Xu Shizhong, Xu Yang, Xu Chenwu

Abstract excerpt

Genomic selection (GS) holds great promise for accelerating breeding progress in plants, and the advancement of across-population GS is essential to realize its full potential. However, conventional across-population GS heavily relies on precisely aligned markers across dense genotypes, whereas the feasibility of using flexible low-density markers remains underexplored. This study developed GS-Impute, a residual...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Genome, Plant

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genomics

Share to Neural Networks, Computer

Share to Plant Breeding

Share to Selection, Genetic

Share to Zea mays

Source

Open in PubMed Open full text

Identifiers

PMID: 41814661
DOI: 10.1016/j.xplc.2026.101821
PMCID: PMC13174246

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.PMID 41814661Read ↗

Rapid, Reference-Free human genotype imputation with denoising autoencoders.Semantic proximity Read ↗

Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.Semantic proximity Read ↗

Rapid, Reference-Free Human Genotype Imputation with Denoising AutoencodersSemantic proximity Read ↗

A low-coverage skim-sequencing and imputation pipeline for genomic selection.Semantic proximity Read ↗

Split-Transformer Impute (STI): A Transformer Framework for Genotype ImputationSemantic proximity Read ↗

Genotype-Corrector: improved genotype calls for genetic mapping in F2 and RIL populations.Semantic proximity Read ↗

RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.Semantic proximity Read ↗

Comprehensive Evaluation of Genotype Imputation Tools for Ultra-low-depth Whole-Genome Sequencing Data.Semantic proximity Read ↗

Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.Semantic proximity Read ↗

Imputing Genotypes in Biallelic Populations from Low-Coverage Sequence Data.Semantic proximity Read ↗

A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.Semantic proximity Read ↗

Residual networks without pooling layers improve the accuracy of genomic predictions.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.