Stability of ranked gene lists in large microarray analysis studies

Journal of Biomedicine and Biotechnology

Volumn 2010, Issue , 2010, Pages

  Stiglic, Gregor ^a   Kokol, Peter ^a  

^a UNIVERSITY OF MARIBOR   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOINFORMATICS; GENETIC SELECTION; GENETIC STABILITY; MICROARRAY ANALYSIS; AREA UNDER THE CURVE; BREAST TUMOR; COLON TUMOR; DNA MICROARRAY; FEMALE; GENE EXPRESSION REGULATION; GENETIC DATABASE; GENETICS; HUMAN; TUMOR GENE;

AREA UNDER CURVE; BREAST NEOPLASMS; COLONIC NEOPLASMS; DATABASES, GENETIC; FEMALE; GENE EXPRESSION REGULATION, NEOPLASTIC; GENES, NEOPLASM; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 77955372959     PISSN: 11107243     EISSN: 11107251     Source Type: Journal    
DOI: 10.1155/2010/616358     Document Type: Article

References (30)

1. Mook S., Van't Veer L. J., Rutgers E. J. T., Piccart-Gebhart M. J., Cardoso F., Individualization of therapy using mammaprint: from development to the MINDACT trial Cancer Genomics Proteomics 2007 4 3 147 155
2. Li T., Zhang C., Ogihara M., A comparative study of feature selection and multiclass classfication methods for tissue classification based on gene expression Bioinformatics 2004 20 15 2429 2437
3. Golub T. R., Slonim D. K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J. P., Coller H., Loh M. L., Downing J. R., Caligiuri M. A., Bloomfield C. D., Lander E. S., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring Science 1999 286 5439 531 537
4. Park P. J., Pagano M., Bonetti M., A nonparametric scoring algorithm for identifying informative genes from microarray data Pacific Symposium on Biocomputing 2001 52 63
5. Xing E. P., Jordan M. I., Karp R. M., Feature selection for high-dimensional genomic microarray data Proceedings of the 18th International Conference on Machine Learning 2001 601 608
6. Xu L., Tan A. C., Winslow R. L., Geman D., Merging microarray data from separate breast cancer studies provides a robust prognostic test BMC Bioinformatics 2008 9, article 125
7. Ma S., Empirical study of supervised gene screening BMC Bioinformatics 2006 7, article 537
8. Qiu X., Xiao Y., Gordon A., Yakovlev A., Assessing stability of gene selection in microarray data analysis BMC Bioinformatics 2006 7 1, article 50
9. Ein-Dor L., Zuk O., Domany E., Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer Proceedings of the National Academy of Sciences of the United States of America 2006 103 15 5923 5928
10. Bar-Ilan J., Mat-Hassan M., Levene M., Methods for comparing rankings of search engine results Computer Networks 2006 50 10 1448 1463
11. Fagin R., Kumar R., Sivakumar D., Comparing top k lists Proceedings of the 4th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '03) 2003 Baltimore, Md, USA 28 36
12. Barrett T., Troup D. B., Wilhite S. E., Ledoux P., Rudnev D., Evangelista C., Kim I. F., Soboleva A., Tomashevsky M., Edgar R., NCBI GEO: mining tens of millions of expression profiles-database and tools update Nucleic Acids Research 2007 35 1 D760 D765
13. Witten I. H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques 2005 2nd San Mateo, Calif, USA Morgan Kaufmann Morgan Kaufmann Series in Data Management Systems
14. Shannon C., Weaver W., Mathematical Theory of Communication 2002 Urbana, Ill, USA University of Illinois Press
15. Quinlan J. R., Induction of decision trees Machine Learning 1986 1 1 81 106
16. Fayyad U. M., Irani K. B., On the handling of continuous-valued attributes in decision tree generation Machine Learning 1992 8 1 87 102
17. Kira K., Rendell L. A., A practical approach to feature selection Proceedings of the 9th International Workshop on Machine learning 1992 San Mateo, Calif, USA 249 256
18. Kononenko I., Estimating attributes: analysis and extensions of relief Proceedings of the European Conference on Machine Learning 1994 Catania, Italy 171 181
19. Sikonja M. R., Kononenko I., An adaptation of relief for attribute estimation in regression Proceedings of the 4th International Conference on Machine Learning (ICML '97) 1997 Corvalis, Ore, USA 296 304
20. Guyon I., Weston J., Barnhill S., Vapnik V., Gene selection for cancer classification using support vector machines Machine Learning 2002 46 13 389 422
21. Yeang C.-H., Ramaswamy S., Tamayo P., Mukherjee S., Rifkin R. M., Angelo M., Reich M., Lander E., Mesirov J., Golub T., Molecular classification of multiple tumor types Bioinformatics 2001 17,supplement 1 S316 S322
22. Vapnik V. N., The Nature of Statistical Learning Theory 1999 London, UK Springer Information Science and Statistics
23. Platt J., Scholkopf B., Burges J. C., Smola A. J., Sequential minimal optimization: a fast algorithm for training support vector machines Advances in Kernel MethodsSupport Vector Learning 1999 Cambridge, Mass, USA MIT Press 185 208
24. Van't Veer L. J., Dai H., Van de Vijver M. J., He Y. D., Hart A. A. M., Mao M., Peterse H. L., Van Der Kooy K., Marton M. J., Witteveen A. T., Schreiber G. J., Kerkhoven R. M., Roberts C., Linsley P. S., Bernards R., Friend S. H., Gene expression profiling predicts clinical outcome of breast cancer Nature 2002 415 6871 530 536
25. Ambroise C., McLachlan G. J., Selection bias in gene extraction on the basis of microarray gene-expression data Proceedings of the National Academy of Sciences of the United States of America 2002 99 10 6562 6566
26. Simon R., Radmacher M. D., Dobbin K., McShane L. M., Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification Journal of the National Cancer Institute 2003 95 1 14 18 (Pubitemid 36124291)
27. Hardin D., Tsamardinos I., Aliferis C. F., A theoretical characterization of linear SVM-based feature selection Proceedings of the 21ST International Conference on Machine Learning (ICML '04) July 2004 Alberta, Canada ACM 377 384
28. Simon R., Development and evaluation of therapeutically relevant predictive classifiers using gene expression profiling Journal of the National Cancer Institute 2006 98 17 1169 1171
29. Subramanian A., Tamayo P., Mootha V. K., Mukherjee S., Ebert B. L., Gillette M. A., Paulovich A., Pomeroy S. L., Golub T. R., Lander E. S., Mesirov J. P., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proceedings of the National Academy of Sciences of the United States of America 2005 102 43 15545 15550
30. Stiglic G., Bajgot M., Kokol P., Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays BMC Bioinformatics 2010 11, article 176