Gene Correlation Guided Gene Selection for Microarray Data Classification.
The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high...
| Published in: | BioMed Research International pp. 1 - 12 |
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| Main Authors: | , |
| Format: | equations & formulas pictorial tables/charts Journal Article |
| Published: |
Wiley-Blackwell
8/16/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151928174&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151928174 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/16/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 151928174 151928174 151928174 10.1155/2021/6490118 151928174 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Gene Correlation Guided Gene Selection for Microarray Data Classification. aug: au: Yang, Dong Zhu, Xuchang affil: Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin 300121, China sug: subj: Biochips Genetic Engineering ab: The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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