Enhancing feed efficiency prediction in beef cattle through metabolite profile and artificial intelligence techniques

• Grant number: 24/02625-2
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: August 01, 2024
• End date: November 30, 2024
• Field of knowledge: Agronomical Sciences - Agronomy
• Principal Investigator: Rafael Vieira de Sousa
• Grantee: Lucas Basolli Borsatto
• Supervisor: Haipeng Yu
• Host Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil
• Institution abroad: University of Florida, Gainesville (UF), United States
• Associated to the scholarship: 23/11281-2 - Evaluation of feed efficiency in beef cattle using artificial intelligence and metabolite profile, BP.IC

Abstract

Feed efficiency, defined as an animal's ability to convert feed into desired products, has important impacts on the sustainability and economic viability of beef production systems. Although various methods have been developed to quantify feed efficiency, accurately assessing feed efficiency remains to be challenged due to its complexity. Recent advances in metabolomics offer a great opportunity for predicting feed efficiency in beef cattle. The objective of this study is to explore the application of artificial intelligence models for feed efficiency prediction in beef cattle using metabolite data. An existing dataset consisting of metabolite information from 64 Black Angus cattle will be used in this study. Using various artificial intelligence models, we will first select the metabolite features that are highly correlated with feed efficiency, which will be then fitted into the models for predicting feed efficiency. All analyses will be conducted using the Python programming language.