Genomic sweeping for hypermethylated genes

Bioinformatics

Volumn 23, Issue 3, 2007, Pages 281-288

  Goh, Liang ^a   Murphy, Susan K ^a   Muhkerjee, Sayan ^a   Furey, Terrence S ^a  

^a DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ONCOPROTEIN;

ACCESS TO INFORMATION; ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; CANCER GENETICS; CLUSTER ANALYSIS; COMPUTER ANALYSIS; COMPUTER PROGRAM; CPG ISLAND; GENE IDENTIFICATION; GENOMICS; NUCLEOTIDE SEQUENCE; OVARY CANCER; PREDICTION; PRIORITY JOURNAL; PROTEIN METHYLATION; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; VALIDATION PROCESS;

EID: 33847266422     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btl620     Document Type: Article

References (34)

1. Adorjan,P. et al. (2002) Tumour class prediction and discovery by microarray-based DNA methylation analysis. Nucleic Acids Res., 30, e21.
2. Bhasin,M. et al. (2005) Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett., 579, 4302-4308.
3. Bibikova,M. et al. (2006) High-throughput DNA methylation profiling using universal bead arrays. Genome Res., 16, 383-393.
4. Bock,C. et al. (2006) CpG island methylation in human lymphocytes is highly correlated with DNA sequence, repeats, and predicted DNA structure. PLoS Genet., 2, e26.
5. Cardie,C. and Howe,N. (1997) Improving minority class prediction using case-specific feature weights. In Kaufmann,M. (ed.), International Conference on Machine Learning. AAAI Press, pp. 57-65.
6. Chawla,N. (2003) C4.5 and imbalanced data sets: Investigating the effect of sampling method, probabilistic estimate, and decision tree structure. In Chawla,N. (ed.), International Conference on Machine Learning, Washington DC, AAAI Press.
7. Chen,C. et al. (2004) Using random forest to learn imbalanced data. Technical Report.
8. Choe,W. et al. (2000) Neural network schemes for detecting rare events in human genomic DNA. Bioinformatics, 16, 1062-1072.
9. Dudoit,S. and Fridlyand,J. (2002) A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol., 3, 1-21.
10. Feltus,F.A. et al. (2003) Predicting aberrant CpG island methylation. Proc. Natl Acad. Sci. USA, 100, 12253-12258.
11. Feltus,F.A. et al. (2006) DNA motifs associated with aberrant CpG island methylation. Genomics, 87, 572-579.
12. Freund,Y. and Schapire,R. (1996) Experiments with a new boosting algorithm. In Chawla,N. (ed.), Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, pp. 148-156.
13. Gardiner-Garden,M. and Frommer,M. (1987) CpG islands in vertebrate genomes. J. Mol. Biol., 196, 261-282.
14. Greally,J.M. (2002) Short interspersed transposable elements (SINEs) are excluded from imprinted regions in the human genome. Proc. Natl Acad. Sci. USA, 99, 327-332.
15. Guo,H. and Viktor,H.L. (2004) Learning from imbalanced data sets with boosting and data generation: The databoost-im approach. SIGKDD Explor. Newsl., 6, 30-39.
16. Hatada,I. et al. (2006) Genome-wide profiling of promoter methylation in human. Oncogene, 25, 3059-3064.
17. Huang,Z. et al. (2006) High-throughput detection of m6p/igf2r intronic hypermethylation and LOH in ovarian cancer. Nucleic Acids Res., 34, 555-563.
18. Japkowicz,N. et al. (1995) A novelty detection approch to classification. In Proceedings of the Fourteenth Joint Conference on Artificial Intelligence. pp. 518-523.
19. Japkowicz,N. (2003a) Class imbalances: Are we focusing on the right issue? In Chawla,N. (ed.), International Conference on Machine Learning, Washington DC, AAAI Press.
20. Japkowicz,N. (2003b) Learning from imbalanced data sets: A comparison of various strategies. In Chawla,N. (ed.), International Conference on Machine Learning, Washington DC, AAAI Press.
21. Joshi,M.V. et al. (2001) Evaluating boosting algorithms to classify rare classes: Comparison and improvements. In Chawla,N. (ed.), International Conference on Data Mining, Washington DC, AAAI Press.
22. Kent,W. et al. (2002) The human genome browser at UCSC. Genome Res., 12, 996-1006.
23. Kubat,M. et al. (1998) Machine learning for the detection of oil spills in satellite radar images. Mach. Learn., 30, 195-215.
24. Laird,P.W. (2003) The power and the promise of DNA methylation markers. Nat. Rev. Cancer, 3, 253-266.
25. Luedi,P.P. et al. (2005) Genome-wide prediction of imprinted murine genes. Genome Res., 15, 875-884.
26. Pednault,E.P.D. et al. (2000) Handling imbalanced data sets in insurance risk modeling. In Japkowicz,N. (ed.), AAAI Workshop. Austin, Texas, AAAI Press, pp. 58-63.
27. Plant,C. et al. (2006) Enhancing instance-based classification with local density: A new algorithm for classifying unbalanced biomedical data. Bioinformatics, 22, 981-988.
28. Qian,J. et al. (2003) Prediction of regulatory networks: Genome-wide identification of transcription factor targets from gene expression data. Bioinformatics, 19, 1917-1926.
29. Robertson,K.D. (2005) DNA methylation and human disease. Nat. Rev. Genet., 6, 597-610.
30. Rollins,R.A. et al. (2006) Large-scale structure of genomic methylation patterns. Genome Res., 16, 157-163.
31. Wang,Z. et al. (2006) Evidence of influence of genomic DNA sequence on human X chromosome inactivation. PLoS Comp. Biol., In press.
32. Weber,M. et al. (2005) Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet., 37, 853-862.
33. Yeo,G.W. et al. (2005) Identification and analysis of alternative splicing events conserved in human and mouse. Proc. Natl Acad. Sci. USA, 102, 2850-2855.
34. Zhang,J. et al. (2004) Learning rules from highly unbalanced data sets. In IEEE International Conference on Data Mining, Brighton, UK, IEEE Computer Society Press.