Prediction of dry eye disease using metabolomics data

Development of methods to improve metabolomics data handling for predicting dry eye disease.

Master

Utilizing the TwinsUK database, we're examining a metabolomics dataset to predict dry eye disease from 901 metabolites in 1,500 participants. We've successfully predicted dry eye using machine learning. To enhance predictions, we're refining data pre-processing. Our improvements target: 1. Outliers: Assessing outlier impact, considering various handling methods. 2. Imputation: With 10.4% missing data, we're examining imputation techniques, considering why data might be missing. 3. Standardization/Normalization: Evaluating different methods and their effects on predictions. Additional exploration areas: 4. Evaluation of Slope: Using "Slope" to identify significant metabolites in dry eye prediction. 5. Bootstrap Methods: Ensuring robust results and controlling cofactors. 6. Explainable AI Methods: Investigating interactions between metabolites and their significance to dry eye disease.