Multi-omics predictive model
Aug 2024 - Current (2 years 2 months)
Building a machine learning model to predict growth rate of yeast strains • Modifying existing predictive models for obtaining reaction rates of metabolic reactions by utilizing proteomic measurements • Developing a novel and comprehensive machine learning model to predict growth rate using integrative omic data (fluxomic, transcriptomic, proteomic, and metabolomic) for improved accuracy, utilizing MATLAB and Python as a part of my master's thesis Deployed a iMDB Sentiment Analysis model as a ML product employing MLOps • Collaborated to develop and deploy a ML model using Apache Spark and Airflow to systematically process, train and deploy the model • Tracked parameters of model using MLFlow to optimize parameters which were quantified