Background In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of dif‑ferent diseases, including cancer and neurodegenerative disorders. For rare diseases,... Show moreBackground In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of dif‑ferent diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirementfor large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder causedby a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational studyfor HD, Enroll‑HD, describes over 21,000 participants. As such, Enroll‑HD is amenable to ML methods. In this study, wepre‑processed and imputed Enroll‑HD with ML methods to maximise the inclusion of participants and variables. Withthis dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well‑established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of MLmethods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments.Results Simple pre‑processing imputed around 42% of missing values in Enroll‑HD. Also, 167 variables were retainedas a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula.The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehnformula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accu‑rate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predictedwith an accuracy of 85.2%. The resulting pre‑processing workflow and code to train the ML models are available to beused for related HD predictions at: https:// github. com/ Jaspe rO98/ hdml/ tree/ main.Conclusions Our pre‑processing workflow made it possible to resolve the missing values and include most par‑ticipants and variables in Enroll‑HD. We show the added value of a ML approach, which improved AAO predictionsand allowed for the development of an advisory model that can assist clinicians and participants in estimating futuredriving capability. Show less
The chapters that comprised this thesis cover a broad range of subjects from analytical method development to clinical application of metabolic profiling. They are united by the facts that all of... Show moreThe chapters that comprised this thesis cover a broad range of subjects from analytical method development to clinical application of metabolic profiling. They are united by the facts that all of these studies aimed at analysis of biological fluids and that the presented methods and approaches may ultimately become parts of a robust metabolomics workflow that might be used in a future personalized medicine. Show less
