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Improving Diseases Predictions Utilizing External Bio-Banks by Pinto, Hido; Segal, Eran is a scholarly article available to read on EtoBox.

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Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is often constrained by the lack of their available disease labels. In this research, we demonstrate how machine learning can be leveraged to enhance explainability and uncover biologically meaningful associations, even when predictive improvements in disease modeling are limited. We train LightGBM models from scratch on our dataset (10K) to impute metabolomics features and apply them to the UK Biobank (UKBB) for downstream analysis. The imputed metabolomics features are then used in survival analysis to assess their impact on disease-related risk factors. As a result, our approach successfully identified biologically relevant connections that were not previously known to the predictive models. Additionally, we applied a genome-wide association study (GWAS) on key metabolomics features, revealing a link between vascular dementia and smoking. Although being a well-established epidemiological relationship, this link was not embedded in the model's training data, which validated the method's ability to extract meaningful signals. Furthermore,

Author

Pinto, Hido; Segal, Eran

Published

2025

Language

EN