ArticleTropical animal health and production2025
Predicting genetic merit in Harnali sheep using machine learning techniques.
Article in Tropical animal health and production, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
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Who cites it
2 citing papers in PubMed.
- Evaluating machine learning algorithms for accurate prediction of yearling weight in Magra sheep.Tropical animal health and production · 2026Article
- Predicting performance traits in Murrah buffaloes using machine learning: a comparative approach.Tropical animal health and production · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning techniques offer promising avenues for enhancing animal breeding programs by leveraging genomic and phenotypic data to predict valuable traits accurately. In this study, we evaluated seven machine learning algorithms viz., K-nearest Network (KNN), Multiple Linear Regression (MLR), Bayesian Regression (BR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Gradient Boosting Machine (GBM) for predicting genetic merits of Harnali sheep using pedigree and phenotypic information. The dataset comprised records of 2036 Harnali lambs spanning from 1998 to 2021, with predictors including pedigree records, birth year, sex, weight at lambing, birth weight, weaning weight, average daily gain, and six-month body weight. Breeding values for six-month body weight were estimated using the restricted maximum likelihood method under Wombat software. Machine learning algorithms were trained and tested on a 75% train set and 25% test set, respectively. The algorithms were compared based on goodness of fit criteria including coefficient of determination (R
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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.