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FS-GBDT : identific...
FS-GBDT : identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT
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- Zhang, Jialin (author)
- Shandong University
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- Xu, Da (author)
- Shandong University
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- Hao, Kaijing (author)
- Shandong University
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- Zhang, Yusen (author)
- Shandong University
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- Chen, Wei (author)
- Shandong University
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- Liu, Jiaguo (author)
- Shandong University
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- Gao, Rui (author)
- Shandong University
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- Wu, Chuanyan (author)
- Shandong Management University
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- De Marinis, Yang (author)
- Lund University,Lunds universitet,-lup-obsolete,Forskargrupper vid Lunds universitet,Translationell muskelforskning,LUDC (Lund University Diabetes Centre)-lup-obsolete,Lund University Research Groups,Translational Muscle Research
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(creator_code:org_t)
- 2020-09-07
- 2021
- English.
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In: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1477-4054 .- 1467-5463. ; 22:3
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http://dx.doi.org/10...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Keyword
- bioinformatics
- cancer classification
- decision support systems
- feature gene selection
Publication and Content Type
- art (subject category)
- ref (subject category)
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