Housekeeping Gene Expression Normalization in Transcriptomics Mitigates Data Leakage in Machine Learning Models (opens in new tab)

Background: Inappropriate normalization can lead to data leakage and overfitting in machine learning models. Accurately identifying housekeeping genes (HKGs) can overcome this problem and is crucial for normalizing gene expression data, particularly in RNA-Seq experiments. Results: First, we demonstrate that the gene expression of commonly used HKGs significantly changes over time due to immunosuppressive treatments in transplant recipients. Using large public transcriptomic datasets of kidne...