SynthesisMethods in molecular biology (Clifton, N.J.)2022
Genome-Enabled Prediction Methods Based on Machine Learning.
Synthesis in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Inferring genetic values and variances from pre-processed phenotypes.Scientific reports · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits.The plant genome · 2023Article
- Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat.International journal of molecular sciences · 2023Article
- How Plants Tolerate Salt Stress.Current issues in molecular biology · 2023Review
- Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes.Scientific reports · 2022Article
- Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.International journal of molecular sciences · 2022Review
- Transposable element polymorphisms improve prediction of complex agronomic traits in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Genomic prediction in plants: opportunities for ensemble machine learning based approaches.F1000Research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem.
Indexed as
Identifiers
35451777What Socratic 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 Socratic graph.