ArticleFrontiers in genetics2020
Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms.
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 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Article
- Incorporating information of causal variants in genomic prediction using GBLUP or machine learning models in a simulated livestock population.Journal of animal science and biotechnology · 2025Article
- Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2025Article
- Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms.BMC genomics · 2025Article
- DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data.Animals : an open access journal from MDPI · 2023Article
- Classification of breed combinations for slaughter pigs based on genotypes-modeling DNA samples of crossbreeds as fuzzy sets from purebred founders.Frontiers in genetics · 2023Article
- Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information.Journal of agricultural and food chemistry · 2022Article
- Review: optimizing genomic selection for crossbred performance by model improvement and data collection.Journal of animal science · 2021Review
- Prediction of Hanwoo Cattle Phenotypes from Genotypes Using Machine Learning Methods.Animals : an open access journal from MDPI · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced genotyping cost. Data consisted of average daily gain (ADG) and residual feed intake (RFI) records and genotypes of 5,708 purebred (PB) boars and 5,007 CB pigs. Prediction models were fitted using individual PB genotypes and phenotypes (
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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.