Prediction of dry eye disease using metabolomics data

Prediction of dry eye disease using metabolomics data

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

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.


Refine methods for outlier handling, data imputation, and standardization.

Learning outcome

  • Machine Learning
  • Outlier detection
  • Data imputation
  • Data standardization
  • Medical applications


  • Python programming
  • Knowledge about machine learning is an advantage


  • Hugo Hammer
  • Michael Riegler

Collaboration partners

  • Leif Hynnekleiv, OsloMet


Associated contacts

Hugo Hammer

Adjunct Chief Research Scientist

Michael Riegler

ProfessorChief Research Scientist/Research Professor