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  • 이제근 교수 구본일 동문, Briefings in Bioinformatics에 논문 게재

    • 등록일
      2021.05.11
    • 조회수
      1355

Prediction of tumor purity from gene expression data using machine learning

 Bonil Koo, Je-Keun Rhee

 

Abstract

 

Motivation

 

Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity.

 

 

Results

 

We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system.

 

 

Availability

 

The machine learning models constructed for this study are available athttps://github.com/BonilKoo/ML_purity.

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