Biomarker selection for pediatric sepsis diagnosis using deep learning

This study proposes a new approach for diagnosing pediatric sepsis that utilizes a convolutional neural network and a combination of 7 immune-related genes (IRGs), including CD24, TTK, PRG2, CLEC7A, CCL3, TNFAIP3, and CCRL2. A three-layer gene selection process involves a sequential procedure that combines differential gene expression analysis, selection of immune-related genes, and gene score calculation using the F-score algorithm.