FS-GBDT : identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT
(2021) In Briefings in Bioinformatics 22(3).- Abstract
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... (More)
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.
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- author
- Zhang, Jialin ; Xu, Da ; Hao, Kaijing ; Zhang, Yusen ; Chen, Wei ; Liu, Jiaguo ; Gao, Rui ; Wu, Chuanyan and De Marinis, Yang LU
- organization
- publishing date
- 2021-05-20
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- bioinformatics, cancer classification, decision support systems, feature gene selection
- in
- Briefings in Bioinformatics
- volume
- 22
- issue
- 3
- publisher
- Oxford University Press
- external identifiers
-
- pmid:34020547
- scopus:85106746209
- ISSN
- 1477-4054
- DOI
- 10.1093/bib/bbaa189
- language
- English
- LU publication?
- yes
- id
- 64eed584-27bb-4118-8ade-56da92cca050
- date added to LUP
- 2021-06-08 16:03:57
- date last changed
- 2024-09-08 19:12:56
@article{64eed584-27bb-4118-8ade-56da92cca050, abstract = {{<p>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.</p>}}, author = {{Zhang, Jialin and Xu, Da and Hao, Kaijing and Zhang, Yusen and Chen, Wei and Liu, Jiaguo and Gao, Rui and Wu, Chuanyan and De Marinis, Yang}}, issn = {{1477-4054}}, keywords = {{bioinformatics; cancer classification; decision support systems; feature gene selection}}, language = {{eng}}, month = {{05}}, number = {{3}}, publisher = {{Oxford University Press}}, series = {{Briefings in Bioinformatics}}, title = {{FS-GBDT : identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT}}, url = {{http://dx.doi.org/10.1093/bib/bbaa189}}, doi = {{10.1093/bib/bbaa189}}, volume = {{22}}, year = {{2021}}, }