Table 2.

Applications of Artificial Intelligence and Machine Learning in Animal Biotechnology.

Description	Citation
Applications of machine learning in livestock management
Analyzing Internal and External Factors in Livestock Supply Forecasting: This study uses machine learning algorithms to predict future livestock values, focusing on sustainability in the pork market.	( 1 )
Predictive Models for Livestock Output: This research applies machine learning techniques to predict livestock outputs, utilizing various predictors such as livestock units and costs to improve forecasting.	( 2 )
Predicting the Weight of Livestock: A machine learning model that utilizes algorithms like Random Forest and Ridge Regression to predict livestock weight based on different input features.	( 5 )
Applications of machine learning in the field of animal genetics
Identification of Potential Feature Genes for Drug Efficacy in Non-Alcoholic Steatohepatitis Animal Model: This study uses machine learning algorithms to identify key genes that predict treatment responses, focusing on genetic and epigenetic RNA markers in an animal model.	( 7 )
Genomic Prediction of Cow Behavioral Traits Using Machine Learning: This research focuses on predicting behavioral traits in Holstein cattle, using data from automated milking systems and machine learning models to enhance genetic selection.	( 9 )
Genomic Prediction in Chickens Using Bioinformatics and Machine Learning: Integrating bioinformatics and machine learning, this research aims to predict genetic pathways in chickens, identifying crucial genes related to growth and other traits.	( 11 )
Recent advances in animal cloning coupled with machine learning
Serine Protease Inhibitor Identification Using Machine Learning: This research utilized machine learning strategies to identify and analyze serine protease inhibitors, key proteins that play a role in cloning experiments in the animal's physiological context.	( 13 )
In-Silico Cloning for Vaccine Constructs against Bovine Coronavirus: This study applied machine learning for immunogenic epitope mapping and in-silico cloning to expedite the creation of vaccine constructs, focusing on expression vectors for animal applications.	( 15 )
Mapping Protective Precision Vaccines using Machine Learning: Researchers applied machine learning to structural proteomics and in-silico cloning to develop precision vaccines for Mycoplasma pulmonis, optimizing the vaccine before animal testing.	( 17 )
The integration of AI & ML in embryo transfer technologies
Time-lapse Imaging to Differentiate Embryos: This study uses machine learning algorithms to analyze time-lapse imaging and differentiate embryos from young and old mice for more efficient embryo transfer, with or without preimplantation genetic testing.	( 19 )
Spectroscopy and Machine Learning for Bovine Embryo Grading: The research focuses on integrating imaging, spectroscopy, and machine learning models to automatically predict embryo quality, aiming to improve pregnancy success rates in bovine embryo transfer.	( 21 )
AI-Assisted Embryo Selection in Newts: This study developed an AI-assisted system for selecting viable embryos in Iberian ribbed newts, which is used for fetal development toxicity testing in embryo transfer technologies.	( 23 )
Selective Breeding and AI
Machine Learning for Genomic Selection in Pacific White Shrimp: This study evaluates machine learning methods for genomic selection, focusing on growth traits in Pacific white shrimp, enhancing selective breeding programs.	( 25 )
Machine Learning and Survival Traits in Olive Flounder: This study compares machine learning models with traditional methods for genomic selection related to viral resistance traits in olive flounder, enhancing survival traits.	( 27 )
Bioinformatics in Animal Breeding: A review discussing the integration of bioinformatics and machine learning in animal breeding and genetics to enhance the accuracy of selective breeding programs.	( 29 )
Machine Learning Techniques for Enhancing Accuracy of prediction
Prediction of Growth and Feed Efficiency in Mink: This study applies machine learning algorithms to predict growth and feed efficiency traits in mink, enhancing the accuracy of predictions for selective breeding programs.	( 31 )
Genomic Prediction of Cow Behavioral Traits: Machine learning methods, including CNN and MLP, are used to predict cow behavioral traits in Holstein cattle, achieving moderate accuracies, with CNN showing the highest accuracy.	( 9 )
Animal Healthcare and Diagnostic Accuracy: A review on the role of machine learning in animal healthcare, emphasizing its ability to improve diagnostic accuracy in various animal health-related applications.	( 34 )
Aquaculture and AI
ANN Algorithm
Random Forest: Used to identify aquaculture ponds and optimize aquaculture area management	( 36 )
Deep Learning: Image dataset for fish disease detection to ensure aquaculture health	( 38 )
Decision Trees: AI-based fish growth prediction and optimization of water quality	( 40 )
Artificial Intelligence and Machine Learning in forest animals
Study examining vertical foraging niches in mammals and birds using functional traits and phylogenetic data to understand ecological and evolutionary patterns.	( 41 )
Deep learning-based model called Deep Indel for predicting outcomes of CRISPR/Cas9 genome editing with improved accuracy and interpretability.	( 43 )
Research on the local adaptation of Aedesaegypti mosquitoes, highlighting genomic variations linked to environmental conditions.	( 45 )
Investigation of microbial compositions in ticks from neotropical forest fragments, analyzing intrinsic and extrinsic factors influencing microbiome structure.	( 46 )