A global learning with local preservation method for microarray data imputation
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- 作者：Ye Chena ; ye1991214@126.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Aiguo Wanga ; b ; wangaiguo2546@163.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Huitong Dinga ; ding_huitong@163.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Xia Quea ; quexia@hfut.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Yabo Lic ; liya19890211@126.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Ning Ana ; ning.g.an@acm.org" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Lili Jiangd ; lili.jiang@cs.umu.se" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Missing value imputation ; Microarray data ; Global learning ; Local preservation ; Regression model
- 刊名：Computers in Biology and Medicine
- 出版年：2016
- 出版时间：1 October 2016
- 年：2016
- 卷：77
- 期：Complete
- 页码：76-89
- 全文大小：4815 K
文摘
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We propose a novel method for imputing missing values in microarray data.
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The proposed method works in a global learning with local preservation scheme.
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We design a strategy for automatically selecting similar genes.
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Experimental results demonstrate its effectiveness on real-world datasets.