<ul><li>Machine learning can enhance disease predictions and uncover biologically meaningful associations, even with limited data.</li><li>LightGBM models trained on a dataset of 10K are used to impute metabolomics features.</li><li>Survival analysis is applied to assess the impact of imputed metabolomics features on disease-related risk factors.</li><li>Integration of survival analysis and genetic studies with machine learning can uncover valuable biomedical insights.</li></ul>

Improving Diseases Predictions Utilizing External Bio-Banks

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