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

BMC Bioinformatics
Fangjun LiDongqi Tang

Abstract

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. 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. (ii) We proposed a joint gene selection algorithm by combining the Fast-Correlation-Based Filter (FCBF) search with the information value, the linear correlation coefficient, and variance inflation factor, and removed irrelevant/redundant features. Then the logistic regression-based feature selection method was used to determine influencing factors. (iii) Classification models were developed using machine learning algorithms. This method is evaluated on RNA expression value ...Continue Reading

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Citations

Nov 21, 2020·Translational Oncology·Ying XieElaine Lai-Han Leung

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Methods Mentioned

BETA
RNAseq

Software Mentioned

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Sklearn
Weka
FCBFSearch
RSEM
DAVID

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