Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer.

ABSTRACT: Cervical lymph node metastasis is the leading cause of poor prognosis in oral tongue squamous cell carcinoma and also occurs in the early stages. The current clinical diagnosis depends on a physical examination that is not enough to determine whether micrometastasis remains. The transcriptome profiling technique has shown great potential for predicting micrometastasis by capturing the dynamic activation state of genes. However, there are several technical challenges in using transcriptome data to model patient conditions: (1) An Insufficient number of samples compared to the number of genes, (2) Complex dependence between genes that govern the cancer phenotype, and (3) Heterogeneity between patients between cohorts that differ geographically and racially. We developed a computational frame

SUBMITTER: Kim M 

PROVIDER: S-EPMC8671417 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature