A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification

Cancer Informatics

Volumn 2, Issue , 2006, Pages

  Peng, Yanxiong ^a,b   Li, Wenyuan ^a,b   Liu, Ying ^a,b  

^a UNIVERSITY OF TEXAS AT DALLAS   (United States)

^b The University of Texas at Dallas   (United States)

Author keywords

Biomarker discovery; Cancer classification; Gene expression; Gene selection; Microarray

Indexed keywords

BIOLOGICAL MARKER;

ARTICLE; BAYESIAN LEARNING; BREAST CANCER; CANCER CLASSIFICATION; CLASSIFIER; COLON CANCER; DECISION TREE; F TEST CORRELATION DIFFERENCE METHOD; FEATURE SELECTION; GENE EXPRESSION; GENERALIZED MATRIX APPROXIMATION METHOD; GENETIC SELECTION; INFORMATION GAIN METHOD; INFORMATION PROCESSING; LEUKEMIA; LUNG CANCER; MICROARRAY ANALYSIS; RELIEFF METHOD; SUPPORT VECTOR MACHINE;

EID: 85072725799     PISSN: None     EISSN: 11769351     Source Type: Journal    
DOI: 10.1177/117693510600200024     Document Type: Article

References (28)

1. Blaschke C., Oliveros J.C., and Valencia A., 2001. Mining functional information associated with expression arrays, Functional and Integrative Genomics, 1: 256–268.
2. Blum A.L., and Langley P., 1997. Selection of relevant features and examples in machine learning, Artificial Intelligence, 245–271.
3. Chai H., and Domeniconi C., 2004. An evaluation of gene selection methods for multi-class microarray data classification. Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics. Pisa, Italy.
4. Chu W., Ghahramani Z., Falciani F., and Wild D.L., 2005. Biomarker discovery in microarray gene expression data with Gaussian processes, Bioinformatics, 21: 3385–3393.
5. Cruz J.A., and Wishart D.S., 2006. Applciations of machine learning in cancer prediction and prognosis, Cancer Informatics, 2: 59–78.
6. Das S., 2001. Filters, wrappers and a boosting-based hybrid for feature selection, Proceedings of the International Conference on Machine Learning, 74–81.
7. Diaz-Uriarte R., and Alvarex de Andrez S., 2006. Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 7: 3.
8. Ding C., and Peng H., 2003. Minimum redundancy feaure selection from microarray gene expression data. Proceedings of Computational Systems Bioinformatics. Stanford Unviersity, Stanford CA, 523–529.
9. Donoho D.L., 2000. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality. Math Challenges of the 21st Century. Los Angeles CA, August 6 11, 2000.
10. Dudoit S., Fridlyand J., and Speed T., 2002. Comparison of discrimination methods for the classification of tumors using gene expression, J. Am. Stat. Assoc, 97: 77–87.
11. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., and Mesirov J.P.. 1999. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286: 531–537.
12. Hall M.A., 1999. Correlation based feature selection for machine learning. Doctoral dissertation. Department of Computer Science. University of Waikato.
13. Inza I., Larranaga P., Blanco R., and Cerrolaza A.J., 2004. Filter versus wrapper gene selection approaches in DNA microarray domains, Artificial Intelligence in Medicine, 31: 91–103.
14. Inza I., Sierra B., Blanco R., and Larranaga P., 2002. Gene selection by sequential search wrapper approaches in microarray cancer class prediction, J. Intell. Fuzzy Syst., 12: 25–33.
15. Jaeger J., Sengupta R., and Ruzzo W.L., 2003. Improved gene selection for classification of microarrays. Proceedings of Pacific Symposium on Biocomputing (PSB 2003). Kauai, HI.
16. Kohavi R.S. D., 1995. Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology, Proceeding of The First International Conference on Knowledge Discovery and Data Mining.
17. Li T., Zhang C., and Ogihara M., 2004. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression, Bioinformatics, 20: 2429–2437.
18. Li W., Peng Y., Hang H.-C., and Liu Y., 2006. Efficient generalized matrix approximations for biomarker discovery and visualization in gene expression data., LSS Computational System Bioinformatics (CSB). Stanford University, Stanford, CA.
19. Liu H., Li J., and Wong L., 2002. A comparative study of feature selection and classification methods using gene expression profiles and proteomic patterns, Genome Informatics, 13: 51–60.
20. Liu Y., 2004. A Comparative Study on Feature Selection Methods for Drug Discovery, J. Chem. Inf. Comput. Sci., 44: 1823–1828.
21. Liu Y., Navathe S.B., Civera J., Dasigi V., Ram A., and Ciliax B.J.. 2005. Text mining biomedical literature for discovering gene-to-gene relationships” a comparative study of algorithms, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2: 62–76.
22. Mitchell T., 1997. Machine Learning. McGraw Hill, New York.
23. Robnik-Sikonja M., and Kononenko I., 2003. Theoretical and empirical analysis of ReliefF and RRelieffF, Machine Learning, 53: 23–69.
24. Witten I.H., and Frank E., 2005. Data Mining: Practical machine learning tools and technique. Morgan Kauffman, San Francisco.
25. Xing E.P., Jordan M.I., and Karp R.M., 2001. Feature selection for high-dimensional genomics microarray data. International Conference on Machine Learning.
26. Xu Y., Olman V., and Xu D., 2003. EXCAVATOR: A Computer Program for Efficiently Mining Gene Expression Data, Nucleic Acids Research, 31: 5582–5589.
27. Yang Y., and Pedersen J.P., 1997. A comparative study on feature selection in text categorization. Proceeding of ICML
28. Yu L., and Liu H., 2004. Redundancy based feature selection for microarray data. KDD 2004. Seattle, WA, 737–742.