ArticleComputational and structural biotechnology journal2022
Genomic prediction through machine learning and neural networks for traits with epistasis.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integrated Analysis of SNPs and Structural Variations via High-depth Whole-genome Sequencing Reveals the Genetic Architecture and Optimizes Genomic Prediction in Chickens.Poultry science · 2026Article
- Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation.Frontiers in plant science · 2026Article
- Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs.Frontiers in genetics · 2025Article
- Epistasis and pleiotropy-induced variation for plant breeding.Plant biotechnology journal · 2024Review
- Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.Scientific reports · 2024Article
- Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.Frontiers in plant science · 2024Article
- Optimizing drought tolerance in cassava through genomic selection.Frontiers in plant science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic wide selection (GWS) is one contributions of molecular genetics to breeding. Machine learning (ML) and artificial neural networks (ANN) methods are non-parameterized and can develop more accurate and parsimonious models for GWS analysis. Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible ML methods, automatically modeling nonlinearities and interactions of the predictor variables. This study aimed to evaluate and compare methods based on ANN, ML, including MARS, and G-BLUP through GWS. An F2 population formed by 1000 individuals and genotyped for 4010 SNP markers and twelve traits from a model considering epistatic effect, with QTL numbers ranging from eight to 480 and heritability (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.