Network-based support vector machine for classification of microarray samples

BMC Bioinformatics

Volumn 10, Issue SUPPL. 1, 2009, Pages

  Zhu, Yanni ^a   Shen, Xiaotong ^b   Pan, Wei ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFICATION PROBLEMS; BIOLOGICAL OBSERVATIONS; HIGH DIMENSIONAL DATA; MICROARRAY APPLICATIONS; NETWORK-BASED APPROACH; PREDICTION ACCURACY; PREDICTIVE PERFORMANCE; STATISTICAL FRAMEWORK;

GENES; MICROARRAYS; STATISTICAL MECHANICS; BIOINFORMATICS;

SUPPORT VECTOR MACHINES;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST TUMOR; DNA MICROARRAY; FEMALE; GENE REGULATORY NETWORK; GENETIC DATABASE; GENETICS; HUMAN; HUMAN GENOME; METHODOLOGY; PARKINSON DISEASE;

BREAST NEOPLASMS; DATABASES, GENETIC; FEMALE; GENE REGULATORY NETWORKS; GENOME, HUMAN; HUMANS; NEURAL NETWORKS (COMPUTER); OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PARKINSON DISEASE;

EID: 60849121073     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-S1-S21     Document Type: Conference Paper

References (23)

1. Chuang HY Lee EJ Liu YT Lee DH Ideker T Network-based classification of breast cancer metastasis Mol Syst Biol 2007, 3:140 2063581 17940530 10.1038/msb4100180
2. Frolov AE Godwin AK Favorova OO Differential gene expression analysis by DNA microarray technology and its application in molecular oncology Mol Biol 2003, 37:486-494 10.1023/A:1025166706481
3. Yang TY The simple classification of multiple cancer types using a small number of significant genes Mol Diagn Ther 2007, 11:265-275 17705581
4. Wang Y Klijin JG Zhang Y Sieuwerts AM Look MP Yang F Talantov D Timmermans M Meijer-van Gelder ME Yu J Jatkoe T Berns EM Atkins D Foekens JA Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer Lancet 2005, 365:671-679 15721472
5. Xiong MM Li WJ Zhao JY Li J Boerwinkle E Feature (gene) selection in gene expression-based tumor classification Mol Genet Metab 2001, 73:239-247 10.1006/mgme.2001.3193 11461191
6. Ein-Dor L Kela I Getz G Givol D Domany E Outcome signature genes in breast cancer: Is there a unique set? Bioinformatics 2005, 21:171-178 10.1093/bioinformatics/bth469 15308542
7. Ein-Dor L Zuk O Domany E Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer Proc Natl Acad Sci USA 2006, 103:5923-5928 1458674 16585533 10.1073/pnas.0601231103
8. Liu M Liberzon A Kong SW Lai WR Park PJ Kohane IS Kasif S Network-based analysis of affected biological processes in type 2 diabetes models PLoS Genet 2007, 3:e96 doi:10.1016/S0140-6736(05)17947-1 10.1371/ journal.pgen.0030096 17571924 1904360
9. Cortes C Vapnik V Support-vector networks Machine Learning 1995, 20:273-297
10. Vapnik V The Nature of Statistical Learning Theory New York: Springer 1995
11. Brown MPS Grundy WN Lin D Cristianini N Sugnet CW Furey TS Ares M Haussler D Knowledge-based analysis of microarray gene expression data by using support vector machines Proc Natl Acad Sci USA 2000, 97:262-267 26651 10618406 10.1073/pnas.97.1.262
12. Furey T Cristianini N Duffy N Bednarski DW Schummer M Haussler D Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics 2000, 16:906-914 10.1093/ bioinformatics/16.10.906 11120680
13. Zou H Yuan M The F∞-norm Support Vector Machine Stat Sin 2008, 18:379-398
14. Wahba G Lin Y Zhang H GACV for support vector machines Advances in Large Margin Classifiers Cambridge, MA: MIT Press Smola A, Bartlett P, Scholkopf B, Schuurmans D 2000:297-311
15. Hastie T Tibshirani R Friedman JH The Elements of Statistical Learning New York: Springer 2001
16. Friedman JH Hastie T Rosset S Tibshirani R Zhu J Discussion of boosting papers Ann Appl Stat 2004, 32:102-107
17. Pan W Xie B Shen X Incorporating predictor network in penalized regression with application to microarray data [Manuscript submitted].
18. Li C Li H Network-constrained regularization and variable selection for analysis of genomic data Bioinformatics 2008, 24:1175-1182 10.1093/ bioinformatics/btn081 18310618
19. Zou H Hastie T Regularization and variable selection via the elastic net J R Statist Soc B 2005, 67:301-320 10.1111/j.1467-9868.2005.00503.x
20. Wang L Zhu J Zou H The doubly regularized support vector machine Stat Sin 2006, 16:589-615
21. Gene Expression Omnibus: GSE6613 http://www.ncbi.nlm.nih.gov/geo/
22. Scherzer CR Eklund AC Morse LJ Liao Z Locascio JJ Fefer D Schwarzschild MA Schlossmacher MG Hauser MA Vance JM Sudarsky LR Standaert DG Growdon JH Jensen RV Gullans SR Molecular markers of early Parkinson's disease based on gene expression in blood Proc Natl Acad Sci USA 2007, 104:955-960 1766335 17215369 10.1073/pnas.0610204104
23. KEGG: Parkinson's disease http://cgap.nci.nih.gov/Pathways/Kegg/hsa05020