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

A split-and-merge deep learning approach for phenotype prediction.

Frontiers in bioscience (Landmark edition) - 4 Mar 2022

Huang Wei-Heng, Wei Yu-Chung

Abstract excerpt

BACKGROUND: Phenotype prediction with genome-wide markers is a critical but difficult problem in biomedical research due to many issues such as nonlinearity of the underlying genetic mapping and high-dimensionality of marker data. When using the deep learning method in the small-n-large-p data, some serious issues occur such as over-fitting, over-parameterization, and biased prediction. METHODS: In this study, we...

Read the complete abstract on PubMed

Topics

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

Share to Deep Learning
Share to Neural Networks, Computer
Share to Phenotype

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35345310
DOI: 10.31083/j.fbl2703078

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A split-and-merge deep learning approach for phenotype prediction.PMID 35345310Read ↗

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

Exploring the application of deep learning methods for polygenic risk score estimationSemantic proximity Read ↗

Transfer learning for genotype-phenotype prediction using deep learning models.Semantic proximity Read ↗

Technical note: an R package for fitting sparse neural networks with application in animal breeding.Semantic proximity Read ↗

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes.Semantic proximity Read ↗

Partially connected neural networks for complex trait prediction: application to human height.Semantic proximity Read ↗

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.Semantic proximity Read ↗

DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.Semantic proximity Read ↗

deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle.Semantic proximity Read ↗

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypesSemantic proximity Read ↗

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

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Semantic proximity Read ↗

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