Robust sparse accelerated failure time model for survival analysis.
To identify the bio-mark genes related to disease with high dimension and low sample size gene expression data, various regression approaches with different regularization methods have been proposed to solve this problem. Nevertheless, high-noises in biological data significantly reduce the performa...
| Publicado en: | Technology & Health Care Vol. 26; pp. 55 - 64 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
2018 Supplement 1
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129909153&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129909153 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09287329 3QT jtl: Technology & Health Care issn: 09287329 maglogo: N pubinfo: dt: 2018 Supplement 1 vid: 26 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 129909153 129909153 NLM29689755 10.3233/THC-174141 NLM29689755 129909153 ppf: 55 ppct: 9 formats: tig: atl: Robust sparse accelerated failure time model for survival analysis. aug: au: Shen, Haiwei Chai, Hua Li, Meiping Zhou, Zhiming Liang, Yong Yang, Ziyi Huang, Haihui Liu, Xiaoying Zhang, Bowen Gómez Schwarzacher Zhou affil: Faculty of Information Technology and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China sug: ab: To identify the bio-mark genes related to disease with high dimension and low sample size gene expression data, various regression approaches with different regularization methods have been proposed to solve this problem. Nevertheless, high-noises in biological data significantly reduce the performances of methods. The accelerated failure time (AFT) modelwas designed for gene selection and survival time estimation in cancer survival analysis. In this article, we proposed a novel robust sparse accelerated failure time model (RS-AFT) through combining the least absolute deviation (LAD) and Lq regularization. An iterative weighted linear programming algorithm without regularization parameter tuning was proposed to solve this RS-AFT model. The results of the experiments show our method has better performancebothin gene selection and survival time estimationthan some widely used regularization methods such as lasso, elastic net and SCAD. Hence we thought the RS-AFT model may be a competitive regularization method in cancer survival analysis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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