Literature DB >> 21186245

PUGSVM: a caBIG™ analytical tool for multiclass gene selection and predictive classification.

Guoqiang Yu1, Huai Li, Sook Ha, Ie-Ming Shih, Robert Clarke, Eric P Hoffman, Subha Madhavan, Jianhua Xuan, Yue Wang.   

Abstract

UNLABELLED: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases. AVAILABILITY: http://www.cbil.ece.vt.edu/caBIG-PUGSVM.htm.

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Year:  2010        PMID: 21186245      PMCID: PMC3042183          DOI: 10.1093/bioinformatics/btq721

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

1.  A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns.

Authors:  Huiqing Liu; Jinyan Li; Limsoon Wong
Journal:  Genome Inform       Date:  2002

2.  Analysis of recursive gene selection approaches from microarray data.

Authors:  Fan Li; Yiming Yang
Journal:  Bioinformatics       Date:  2005-08-23       Impact factor: 6.937

Review 3.  The properties of high-dimensional data spaces: implications for exploring gene and protein expression data.

Authors:  Robert Clarke; Habtom W Ressom; Antai Wang; Jianhua Xuan; Minetta C Liu; Edmund A Gehan; Yue Wang
Journal:  Nat Rev Cancer       Date:  2008-01       Impact factor: 60.716

4.  Multiclass cancer classification and biomarker discovery using GA-based algorithms.

Authors:  Jane Jijun Liu; Gene Cutler; Wuxiong Li; Zheng Pan; Sihua Peng; Tim Hoey; Liangbiao Chen; Xuefeng Bruce Ling
Journal:  Bioinformatics       Date:  2005-04-06       Impact factor: 6.937

5.  Multiclass cancer diagnosis using tumor gene expression signatures.

Authors:  S Ramaswamy; P Tamayo; R Rifkin; S Mukherjee; C H Yeang; M Angelo; C Ladd; M Reich; E Latulippe; J P Mesirov; T Poggio; W Gerald; M Loda; E S Lander; T R Golub
Journal:  Proc Natl Acad Sci U S A       Date:  2001-12-11       Impact factor: 11.205

6.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Authors:  T R Golub; D K Slonim; P Tamayo; C Huard; M Gaasenbeek; J P Mesirov; H Coller; M L Loh; J R Downing; M A Caligiuri; C D Bloomfield; E S Lander
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

Review 7.  Approaches to working in high-dimensional data spaces: gene expression microarrays.

Authors:  Y Wang; D J Miller; R Clarke
Journal:  Br J Cancer       Date:  2008-02-19       Impact factor: 7.640

  7 in total
  3 in total

Review 1.  Preclinical magnetic resonance imaging and systems biology in cancer research: current applications and challenges.

Authors:  Chris Albanese; Olga C Rodriguez; John VanMeter; Stanley T Fricke; Brian R Rood; YiChien Lee; Sean S Wang; Subha Madhavan; Yuriy Gusev; Emanuel F Petricoin; Yue Wang
Journal:  Am J Pathol       Date:  2012-12-04       Impact factor: 4.307

2.  Data-driven detection of subtype-specific differentially expressed genes.

Authors:  Lulu Chen; Yingzhou Lu; Chiung-Ting Wu; Robert Clarke; Guoqiang Yu; Jennifer E Van Eyk; David M Herrington; Yue Wang
Journal:  Sci Rep       Date:  2021-01-11       Impact factor: 4.379

3.  Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.

Authors:  Niya Wang; Eric P Hoffman; Lulu Chen; Li Chen; Zhen Zhang; Chunyu Liu; Guoqiang Yu; David M Herrington; Robert Clarke; Yue Wang
Journal:  Sci Rep       Date:  2016-01-07       Impact factor: 4.379

  3 in total

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