An improved clear cell renal cell carcinoma stage prediction model based on gene sets.

ABSTRACT:

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment.

Results

In this study, we developed new strategies to extract important gene features and trained machine learning-based classifiers to predict stages of ccRCC samples. The novelty of our approach is that (i) We improved the feature preprocessing procedure by binning and coding, and increased the stability of data and robustness of the classification model

SUBMITTER: Li F 

PROVIDER: S-EPMC7278205 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature