Advancing Systems Biology Of Yeast Through Machine Learning And Comparative Genomics

This thesis explores the use of machine learning techniques and comparative genomic analysis to gain a deeper insight into yeast traits and metabolism. Machine learning approaches were employed to predict gene essentiality based on sequence features and evolutionary features, a deep learning model was developed to predict enzyme turnover numbers, and a random forest algorithm was used to investigate feature importance analysis on protein production. The results of this thesis provide a better understanding of yeast species and their cellular metabolism.