Deep learning-based model for predicting progression in patients with head and neck squamous cell carcinoma

Abstract excerpt

PURPOSE: This study endeavors to build a deep learning (DL)-based model for predicting disease progression in head and neck squamous cell carcinoma (HNSCC) patients by integrating multi-omics data. METHODS: RNA sequencing, miRNA sequencing, and methylation data from The Cancer Genome Atlas (TCGA) were used as input for autoencoder, a DL approach. An autoencoder-based prognosis model for PFS was built by SVM...

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