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Yang de Marinis

Associate professor


FS-GBDT : identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT


  • Jialin Zhang
  • Da Xu
  • Kaijing Hao
  • Yusen Zhang
  • Wei Chen
  • Jiaguo Liu
  • Rui Gao
  • Chuanyan Wu
  • Yang De Marinis

Summary, in English

Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.


  • LUDC (Lund University Diabetes Centre)-lup-obsolete
  • Genomics, Diabetes and Endocrinology
  • EXODIAB: Excellence in Diabetes Research in Sweden

Publishing year





Briefings in Bioinformatics





Document type

Journal article


Oxford University Press


  • Bioinformatics and Systems Biology


  • bioinformatics
  • cancer classification
  • decision support systems
  • feature gene selection



Research group

  • LUDC (Lund University Diabetes Centre)-lup-obsolete
  • Genomics, Diabetes and Endocrinology


  • ISSN: 1477-4054