Characterization of the effectiveness of reporting lists of small feature sets relative to the accuracy of the prior biological knowledge

Cancer Informatics

Volumn 9, Issue , 2010, Pages 49-60

  Zhao, Chen ^a   Bittner, Michael L ^b   Chapkin, Robert S ^c   Dougherty, Edward R ^a,b,d  

^a Texas AandM University   (United States)

^b TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

^c TEXAS A AND M UNIVERSITY   (United States)

^d UNIVERSITY OF TEXAS   (United States)

Author keywords

Classification; Feature ranking; Ranking power

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; BIOINFORMATICS; CLINICAL EFFECTIVENESS; DATA ANALYSIS; INFORMATION PROCESSING; KERNEL METHOD; KNOWLEDGE; MATHEMATICAL MODEL; METHODOLOGY; PROBABILITY; PROCESS OPTIMIZATION;

EID: 77952897266     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: 10.4137/cin.s4020     Document Type: Article

References (25)

1. Jain A, Zonger D. Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Trans Pattern Analysis and Machine Intelligence. 1997;19:153-8.
2. Kudo M, Sklansky J. Comparison of Algorithms that Select Features for Pattern Classifiers. Pattern Recognition. 2000;33:25-41.
3. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23:2507-17.
4. Ma S, Huang J. Penalized feature selection and classification in bioinformatics. Brief Bioinformatics. 2008;9:392-403.
5. Cover T, Campenhout JV. On the Possible Orderings in the Measurement Selection Problem. IEEE Trans on Systems Man and Cybernetics. 1977;7:657-61.
6. Hughes GF. On the Mean Accuracy of Statistical Pattern Recognizers. IEEE Trans. Information Theory. 1968;14:55-63.
7. Jain AK, Waller WG. On the Optimal Number of Features in the Classification of Multivariate Gaussian Data. Pattern Recognition. 1978;10:365-74.
8. Hua J, Xiong Z, Dougherty ER. Determination of the Optimal Number of Features for Quadratic Discriminant Analysis Via the Normal Approximation to the Discriminant Distribution. Pattern Recognition. 2005a;38:403-21.
9. Hua J, Xiong Z, Lowey J, Suh E, Dougherty ER. Optimal number of features as a function of sample size for various classification rules. Bioinformatics. 2005b;21:1509-15.
10. Sima C, Dougherty ER. The Peaking Phenomenon in the Presence of Feature Selection. Pattern Recognition Letters. 2008;29(11):1667-74.
11. Hua J, Waibhav T, Dougherty ER. Performance of Feature Selection Methods in the Classification of High-Dimensional Data. Pattern Recognition. 2009;42(3):409-424.
12. Sima C, Dougherty ER. What should be expected from feature selection in small-sample settings. Bioinformatics. 2006;22:2430-6.
13. Braga-Neto UM, Dougherty ER. Is cross-validation valid for small-sample microarray classification? Bioinformatics. 2004;20:374-80.
14. Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21:3301-7.
15. Xiao Y, Hua J, Dougherty ER. Quantification of the impact of feature selection on the variance of cross-validation error estimation. EURASIP J Bioinform Syst Biol. 2007;16354.
16. Hanczar B, Hua J, Dougherty ER. Decorrelation of the true and estimated classifier errors in high-dimensional settings. EURASIP J Bioinform Syst Biol. 2007;38473.
17. Grate LR. Many accurate small-discriminatory feature subsets exist in microarray transcript data: biomarker discovery. BMC Bioinformatics. 2005;6:97.
18. Braga-Neto UM. Fads and Fallacies in the Name of Small-Sample Microarray Classification. IEEE Signal Processing Magazine. 2007;24(1):91-9.
19. Sima C, Braga-Neto U, Dougherty ER. Superior feature-set ranking for small samples using bolstered error estimation. Bioinformatics. 2005; 21:1046-54.
20. Kim S, Dougherty ER, Shmulevich I, et al. Identification of combination gene sets for glioma classification. Mol Cancer Ther. 2002;1:1229-36.
21. Morikawa J, Li H, Kim S, et al. Identification of signature genes by microarray for acute myeloid leukemia without maturation and acute promyelocytic leukemia with t(15;17)(q22;q12)(PML/RARalpha). Int J Oncol. 2003;23:617-25.
22. + and CD5-Diffuse Large B-cell Lymphoma and Mantle Cell Lymphoma. Cancer Research. 2003;63:60-6.
23. Zhao C, Ivanov I, Dougherty ER, et al. Non-invasive Detection of Candidate Molecular Biomarkers in Subjects with a History of Insulin Resistance and Colorectal Adenomas. Cancer Prevention Research. 2009;2(6):590-7.
24. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96:434-42.
25. Morgan TM, Krumholz HM, Lifton RP, Spertus JA. Nonvalidation of reported genetic risk factors for acute coronary syndrome in a large-scale replication study. JAMA. 2007;297:1551-61.