Dr, Isfahan University of Technology, Iran
Dr. Zeinab Asgari is a distinguished researcher specializing in genetic and animal breeding. She earned her master’s degree at Tarbiat Modares University, ranking first among her peers. Currently, she is pursuing a Ph.D. in genetic and animal breeding at Isfahan University of Technology (IUT), where she has also been recognized for academic excellence. Her passion for innovation drives her interest in data analysis, machine learning, and genome-wide association studies (GWAS). Dr. Asgari has contributed significantly to the scientific community through her research and publications. She aims to explore advanced methodologies and collaborate internationally to make meaningful contributions to genetics and animal breeding.
Profile
Scopus
Education
Dr. Zeinab Asgari completed her master’s degree in genetic and animal breeding engineering at Tarbiat Modares University, where she achieved first place among her cohort. She began her Ph.D. studies in genetic and animal breeding in 2018 at Isfahan University of Technology (IUT), maintaining her status as an excellent student. Her academic journey reflects a commitment to excellence and innovation. Throughout her education, she has focused on integrating cutting-edge statistical and computational techniques into genetic studies, particularly Bayesian methodologies and machine learning. These skills have empowered her to address complex challenges in animal breeding, such as improving economic traits in livestock and conducting genome-wide association studies (GWAS).
Research Focus
Dr. Asgari’s research centers on genetic and animal breeding with an emphasis on data analysis and machine learning. Her expertise includes genome-wide association studies (GWAS), leveraging Bayesian methodologies to uncover genetic markers influencing traits such as body weight in chickens and health traits in dairy cows. Her work addresses practical challenges in agriculture, aiming to enhance economic and genetic traits in livestock. Dr. Asgari is passionate about applying computational tools and advanced statistical methods to improve breeding programs and contribute to sustainable agricultural practices. Her ongoing efforts involve identifying genetic markers for economically important traits, utilizing retrospective data, and integrating innovative approaches for actionable insights.
Publications
- 🌾 Risk factors and population attributable fractions for displaced abomasum in Iranian dairy cattle: A retrospective analysis of field data.
- 🐓 Bayes factors revealed selection signature for time to market body weight in chicken: A genome-wide association study using BayesCpi methodology.