Multiclass prediction with partial least square regression for gene expression data: applications in breast cancer intrinsic taxonomy.
Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial le...
| Published in: | BioMed Research International Vol. 2013; pp. 248648 - 248649 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Wiley-Blackwell
2013
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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=104016584&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104016584 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104016584 104016584 2012461979 NLM24490149 PMC3893734 104016584 ppf: 248648 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Multiclass prediction with partial least square regression for gene expression data: applications in breast cancer intrinsic taxonomy. aug: au: Huang, Chi-Cheng Tu, Shih-Hsin Huang, Ching-Shui Lien, Heng-Hui Lai, Liang-Chuan Chuang, Eric Y affil: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan ; Cathay General Hospital SiJhih, New Taipei, Taiwan ; School of Medicine, Fu-Jen Catholic University, New Taipei, Taiwan ; School of Medicine, Taipei Medical University, Taipei, Taiwan. sug: subj: Breast Neoplasms Genes Gene Expression Profiling Tumor Markers, Biological Breast Neoplasms Classification Breast Neoplasms Pathology Female Human Regression Prognosis Female ab: Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS) regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n = 535). The agreement between PAM50 centroid-based single sample prediction (SSP) and PLSregression was excellent (weighted Kappa: 0.988) within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed). Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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