Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction

arXiv:2404.14970v1 Announce Type: new
Abstract: Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the sample sizes in expression datasets are usually limited, and the data from different datasets with different gene expressions cannot be easily combined.
This work proposes a novel …

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