Comparison of two output-coding strategies for multi-class tumor classification using gene expression data and latent variable model as binary classifier

Cancer Informatics

Volumn 9, Issue , 2010, Pages 39-48

  Joseph, Sandeep J ^a   Robbins, Kelly R ^a   Zhang, Wensheng ^a   Rekaya, Romdhane ^a,b,c  

^a Rhodes Centre for Animal and dairy Science

^b Institute of Bioinformatics

^c UNIVERSITY OF GEORGIA   (United States)

Author keywords

Binary classifier; Gene expression; Gibbs sampling; Latent variable model; Multi class tumor classification; Supervised classification

Indexed keywords

MESSENGER RNA;

ARTICLE; CANCER CLASSIFICATION; CLASSIFICATION ALGORITHM; DATA BASE; DIAGNOSTIC ACCURACY; DNA MICROARRAY; EXPRESSED SEQUENCE TAG; GENE EXPRESSION; GENE MAPPING; GENETIC VARIABILITY; MATHEMATICAL MODEL; METHODOLOGY; MICROARRAY ANALYSIS; SUPPORT VECTOR MACHINE; TUMOR CLASSIFICATION;

EID: 77952944675     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: 10.4137/cin.s3827     Document Type: Article

References (22)

1. Golub TR, Slonim DK, Tamayo P, et al. C Molecular classification of cancer: Class discovery and class prediction by gene expression prediction. Science. 1999;286:531-7.
2. Ross DT, Seherf U, Eisen MB, et al. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics. 2000;24:227-35.
3. Furey TS, 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;16:906-14.
4. Ramaswamy S, Tamayo P, Rifkin R, et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proceeding of National Academy of Sciences. 2001;98(26):15149-54.
5. Dudoit S, Fridlyand J, Speed T. Comparison of discrimination methods for classification of tumors using gene expression data. J Am Stat Ass. 2002;97:77-87.
6. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics. 2005;21:631-43.
7. Hastie TJ, Tibshirani RJ. Classification by pairwise coupling. In: Jordan MI, Kearnsa MJ, Solla SA, eds. Advances in neural information processing systems. Volume 10, MIT Press, 1998.
8. Allwein EL, Schapire RE, Singer Y. Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research. 2000;1:113-41.
9. Dietterich TG, Bakiri G. Error-correcting output codes: A general method for improving multiclass inductive learning programs. Proceedings of the Ninth National Conference on Artificial Intelligence. AAAI Press. 1991 572-7.
10. Yeang CH, Ramaswamy S, Tamayo P, et al. Molecular classification of multiple tumor types. Bioinformatics. 2001;17(1):S316-22.
11. Pomeroy SL, Tamayo P, Gaasenbeek M, et al. Prediction of central nervous system embryonal tumor outcome based on gene expression. Nature. 2002;415(6870):436-42.
12. Johnson VE, Albert JH. Ordinary data model. Springer: New York, 1999.
13. West M, Blanchette C, Dressman H, et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of National Academy of Sciences. 2001;98(20):11462-7.
14. Li T, Zhang C, Ogihara M. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics. 2004;20:2429-37.
15. Zhang W, Rekaya R, Bertrand JK. Method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer. Bioinformatics. 2006;22:317-25.
16. Leung YY, Chang CQ, Hung YS, Fung PCW. Gene selection for brain cancer classification. Proceedings of the 28th IEEE EMBS Annual Internationl Conference. New York City, USA, Aug 30-Sept 3, 2006.
17. Bagirov AM, Ferguson B, Ivkovic S, Aaunders G, Yearwood J. Modified global k-means algorithm for clustering in gene expression data sets. Bioinformatics. 2003;19:1800-7.
18. Antonov AV, Tetko IV, Mader MT, Budczies J, Mewes HW. Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics. 2004; 20:644-52.
19. Scholkopf B, Smola JA. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
20. Dietterich TG, Bakiri G. Solving multiclass learning problem via errorcorrecting output codes. J Artif Intell Res. 1995;2:263-86.
21. Scholkopf B, Smola JA. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
22. Robbins K, Joseph S, Zhang W, Rekaya R, Bertrand JK. Classification of incipient Alzheimer patients using gene expression data: Dealing with potential misdiagnosis. Online J Bioinformatics. 2006;7:22-31.