AI Medical Compendium Topic

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Breeding

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Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.

Genes
Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increa...

Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers.

Yi chuan = Hereditas
Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly...

Attention module incorporated transfer learning empowered deep learning-based models for classification of phenotypically similar tropical cattle breeds (Bos indicus).

Tropical animal health and production
Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A conv...

Chemometrics methods, sensory evaluation and intelligent sensory technologies combined with GAN-based integrated deep-learning framework to discriminate salted goose breeds.

Food chemistry
The authenticity of salted goose products is concerning for consumers. This study describes an integrated deep-learning framework based on a generative adversarial network and combines it with data from headspace solid phase microextraction/gas chrom...

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Omics : a journal of integrative biology
Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the ...

Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring.

Tropical animal health and production
The purpose of this study was to evaluate the performance of various prediction models in estimating the growth and morphological traits of pure Hair, Alpine × Hair F (AHF), and Saanen × Hair F (SHF) hybrid offspring at yearling age by employing earl...

An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers.

Poultry science
Machine learning (ML) methods have rapidly developed in various theoretical and practical research areas, including predicting genomic breeding values for large livestock animals. However, few studies have investigated the application of ML in broile...

Sex identification in rainbow trout using genomic information and machine learning.

Genetics, selection, evolution : GSE
Sex identification in farmed fish is important for the management of fish stocks and breeding programs, but identification based on visual characteristics is typically difficult or impossible in juvenile or premature fish. The amount of genomic data ...

Standardizing canine breed data in veterinary records is challenging, but computer vision offers an alternative perspective on breed assignment.

American journal of veterinary research
Dog breed is fundamental health information, especially in the context of breed-linked diseases. The standardization of breed terminology across health records is necessary to leverage the big data revolution for veterinary research. Breed can also i...

Genomic selection in pig breeding: comparative analysis of machine learning algorithms.

Genetics, selection, evolution : GSE
BACKGROUND: The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict...