ArticleFrontiers in genetics2020
A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Neural posterior estimation for population genetics.Genetics · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Popfinder: A Highly Effective Artificial Neural Network Package for Genetic Population Assignment.Molecular ecology resources · 2025Article
- Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding.Plants (Basel, Switzerland) · 2024Article
- Genetic Diversity and Population Structure Analyses in Bitter Gourd (Plants (Basel, Switzerland) · 2023Article
- Hybrid autoencoder with orthogonal latent space for robust population structure inference.Scientific reports · 2023Article
- Deep Learning in Population Genetics.Genome biology and evolution · 2023Article
- Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments.Plants (Basel, Switzerland) · 2023Article
- Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.Frontiers in plant science · 2023Article
- MSC-CSMC: A multi-objective semi-supervised clustering algorithm based on constraints selection and multi-source constraints for gene expression data.Frontiers in genetics · 2023Article
- Quantitative evaluation of nonlinear methods for population structure visualization and inference.G3 (Bethesda, Md.) · 2022Article
- OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security.Plants (Basel, Switzerland) · 2021Review
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Authors and funding
5 authors.
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Abstract
Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (
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Registered trials
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