Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification
Joint Authors
Xu, Jiucheng
Mu, Huiyu
Wang, Yun
Huang, Fangzhou
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-31
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology.
However, most of the existing methods have a high time complexity and poor classification performance.
Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman’s rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms.
Supervised locally linear embedding takes into account class label information and improves the classification performance.
Furthermore, Spearman’s rank correlation coefficient is used to remove the coexpression genes.
The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.
American Psychological Association (APA)
Xu, Jiucheng& Mu, Huiyu& Wang, Yun& Huang, Fangzhou. 2018. Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1132066
Modern Language Association (MLA)
Xu, Jiucheng…[et al.]. Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1132066
American Medical Association (AMA)
Xu, Jiucheng& Mu, Huiyu& Wang, Yun& Huang, Fangzhou. Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1132066
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132066