FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.
Brief Bioinform
; 22(3)2021 05 20.
Article
em En
| MEDLINE
| ID: mdl-34020547
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 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.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Algoritmos
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Regulação Neoplásica da Expressão Gênica
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Biologia Computacional
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Perfilação da Expressão Gênica
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Máquina de Vetores de Suporte
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Neoplasias
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Brief Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China