ArticleGenes2019
Sparse Convolutional Denoising Autoencoders for Genotype Imputation.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- ALGI: Sparse Convolutional Denoising Autoencoder Utilizing Local Genomic Information for Genotype Imputation.Animals : an open access journal from MDPI · 2026Article
- GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.Plant communications · 2026Article
- Article
- Advances in haplotype phasing and genotype imputation.Nature reviews. Genetics · 2026Review
- Self-organizing maps for allele specific expression data reconstruction and identification of anomalous genomic regions.Frontiers in bioinformatics · 2026Article
- BiU-Net: A Biologically Informed U-Net for Genotype Imputation.Research square · 2025Article
- KBeagle: An Adaptive Strategy and Tool for Improving Imputation Accuracy and Computation Time.International journal of molecular sciences · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Missing genotype imputation in non-model species using self-organizing maps.Molecular ecology resources · 2025Article
- STICI: Split-Transformer with integrated convolutions for genotype imputation.Nature communications · 2025Article
- Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals.Sensors (Basel, Switzerland) · 2025Article
- Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.Journal of human genetics · 2024Review
- Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.Journal of human genetics · 2024Review
- Deep Learning Methods for Omics Data Imputation.Biology · 2023Review
- Hybrid autoencoder with orthogonal latent space for robust population structure inference.Scientific reports · 2023Article
- sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.Bioinformatics (Oxford, England) · 2023Article
- Article
- Evaluation of vicinity-based hidden Markov models for genotype imputation.BMC bioinformatics · 2022Article
- A deep learning framework for characterization of genotype data.G3 (Bethesda, Md.) · 2022Article
- An autoencoder-based deep learning method for genotype imputation.Frontiers in artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genotype imputation, where missing genotypes can be computationally imputed, is an essential tool in genomic analysis ranging from genome wide associations to phenotype prediction. Traditional genotype imputation methods are typically based on haplotype-clustering algorithms, hidden Markov models (HMMs), and statistical inference. Deep learning-based methods have been recently reported to suitably address the missing data problems in various fields. To explore the performance of deep learning for genotype imputation, in this study, we propose a deep model called a sparse convolutional denoising autoencoder (SCDA) to impute missing genotypes. We constructed the SCDA model using a convolutional layer that can extract various correlation or linkage patterns in the genotype data and applying a sparse weight matrix resulted from the
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.