Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants

Bioinformatics

Volumn 23, Issue 13, 2007, Pages

  Danziger, Samuel A ^a,b   Zeng, Jue ^b   Wang, Ying ^c   Brachmann, Rainer K ^b,c   Lathrop, Richard H ^a,b,d  

^a University of California at Irvine   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d Irvine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN P53;

ACCURACY; ANALYTIC METHOD; CANCER RESEARCH; CASE STUDY; CLASSIFIER; COMPUTER ANALYSIS; COMPUTER MODEL; CONFERENCE PAPER; CONTROLLED STUDY; GENE MUTATION; GENE SEQUENCE; INTERMETHOD COMPARISON; MACHINE LEARNING; MATHEMATICAL COMPUTING; MEDICAL RESEARCH; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROTEIN FUNCTION;

EID: 34547875776     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btm166     Document Type: Conference Paper

References (42)

1. Baroni,T.E. et al. (2004) A global suppressor motif for p53 cancer mutants. Proc. Natl. Acad. Sci. USA, 101, 4930-4935.
2. Blagosklonny,M.V. (2000) p53 from complexity to simplicity: Mutant p53 stabilization, gain-of-function, and dominant-negative effect. Faseb. J., 14, 1901-1907.
3. Brachmann,R.K. (2004) p53 mutants: The achilles' heel of human cancers? Cell Cycle, 3, 1030-1034.
4. Brachmann,R.K. et al. (1996) Dominant-negative p53 mutations selected in yeast hit cancer hot spots. Proc. Natl Acad. Sci. USA, 93, 4091-4095.
5. Brachmann,R.K. et al. (1998) Genetic selection of intragenic suppressor mutations that reverse the effect of common p53 cancer mutations. EMBO J., 17, 1847-1859.
6. Bullock,A.N. and Fersht,A.R. (2001) Rescuing the function of mutant p53. Nat. Rev. Cancer, 1, 68-76.
7. Bykov,V.J. et al. (2003) Small molecules that reactivate mutant p53. Eur. J. Cancer, 39, 1828-1834.
8. Case,D.A. et al. (2004) "AMBER 8". University of California, San Francisco.
9. Cho,Y. et al. (1994) Crystal structure of a p53 tumor suppressor-DNA complex: Understanding tumorigenic mutations. Science 265, 346.
10. Cohn,D.A. et al. (1996) Active learning with statistical models. J. Artif. Intell. Res., 4, 129-14.
11. Danziger, S.A. et al. (2006) Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants. IEEE T. Comput. Biol. Bioinform, 3, 114-125.
12. Erster,S. and Moll,U.M. (2005) Stress-induced p53 runs a transcription-independent death program. Biochem. Biophys. Res. Commun., 331, 843-850.
13. Fleuret,F. (2004) Fast binary feature selection with conditional mutual information. J Mach. Learn. Res., 5, 1521-1555.
14. Friedler,D.B. et al. (2004) Binding of RAD51 and other peptide sequences to a promiscuous, highly electrostatic, binding site in p53. J. Biol. Chem., 280, 8051-8059.
15. Greenblatt,M.S. et al. (1994) Mutations in the p53 tumor suppressor gene: Clues to cancer etiology and molecular pathogenesis. Cancer Res., 54, 4855-4878.
16. Hamroun,D. et al. (2006) The UMD TP53 database and website: Update and revisions. Hum. Mutat., 27, 14-20.
17. Itti,L. and Baldi,P. (2006) Bayesian surprise attracts human attention. Adv. Neural Inform Process. Systems, 18.
18. Hanahan,D. and Weinberg,R.A. (2000) The hallmarks of cancer. Cell, 100, 57-70.
19. Ho,J. and Benchimol,S. (2003) Transcriptional repression mediated by the p53 tumour suppressor. Cell Death Differ., 10, 404-408.
20. Hollstein,M. et al. (1991) p53 mutations in human cancers. Science, 253, 49-53.
21. Jing,F. et al. (2005) A unified framework for image retrieval using keyword and visual features. IEEE T. Image Process., 14 979-989.
22. Jones,R. et al. (2003) Active learning for information extraction with multiple view feature sets. ECML-03 Workshop on Adaptive Text Extraction and Mining.
23. Ko,L.J. and Prives,C. (1996) p53: Puzzle and paradigm. Genes Dev., 10, 1054-1072.
24. Kobayashi,T. et al. (2003) Genetic strategies in Saccharomyces cerevisiae to study human tumor suppressor genes. Methods Mol. Biol., 223, 73-86.
25. Liu,Y. (2004) Active learning with support vector machine applied to gene expression data for cancer classification. J. Chem. Inform. Comput. Sci., 44, 1936-1941.
26. Muthurajan,U.M. et al. (2004) Crystal structures of historic Sin mutant nucleosomes reveal altered protein-DNA interactions. EMBO J. 23, 260-271.
27. Olivier,M. et al. (2002) The IARC TP53 database: New online mutation analysis and recommendations to users. Hum. Mutat., 19 607-614.
28. Park,J. (2004) Convergence and application of online active sampling using orthogonal pillar vectors. IEEE T. Pattern Anal. Mach. Learn. 28, 1197-1207.
29. Parkin,D. et al. (2005) Global cancer statistics, 2002. CA Cancer J Clin., 55, 74-108.
30. Platt,J.C. (1998) Sequential minimum optimization: A fast algorithm for training support vector machines. Microsoft Research Technical Report MSR-TR-98-14.
31. Prives,C. and Hali,P.A. (1999) The p53 pathway. J. Pathol, 187 112-126.
32. Provost,M.S.-T.a.F.J. (2001) Active learning for class probability estimation and ranking. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, Seattle, Washington, pp. 911-920.
33. Qian,H. et al. (2002) Groups of p53 target genes involved in specific p53 downstream effects cluster into different classes of DNA binding sites. Oncogene, 21, 7901-7911.
34. Roy,N. and McCallum,A. (2001) Toward optimal active learning through sampling estimation of error reduction. In Proceedings of 18th International Conference on Machine Learning, pp. 441-448.
35. Seemann,S. et al. (2004) The tumor suppressor gene TP53: implications for cancer management and therapy. Crit. Rev. Clin. Lab. Sci., 41, 551-583.
36. Sigal,A. and Rotter,V. (2000) Oncogenic mutations of the p53 tumor suppressor: The demons of the guardian of the genome. Cancer Res., 60, 6788-6793.
37. Soussi,T. and Beroud,C. (2001) Assessing TP53 status in human tumours to evaluate clinical outcome. Nat. Rev. Cancer, 1, 233-240.
38. Vogelstein,B. and Kinzler,K.W. (2004) Cancer genes and the pathways they control. Nat. Med., 10, 789-799.
39. Vogelstein,B. et al. (2000) Surfing the p53 network. Nature, 408, 307-310.
40. Wang,W. et al. (2003) Restoring p53-dependent tumor suppression. Cancer Biol. Ther., 2, S55-S63.
41. Wei,C.-L. et al. (2006) A global map of p53 transcription-factor binding sites in the human genome. Cell, 124, 207-219.
42. Witten, I.and Frank,E. (2005) Data mining: Practical machine learning tools and techniques. 2nd edn. Morgan Kaufmann Series in Data Management Systems, San Franciso, CA 94111.