Rough set soft computing cancer classification and network: One stone, two birds

Cancer Informatics

Volumn 9, Issue , 2010, Pages 139-145

  Zhang, Yue ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

; Cancer; Classification; Depended degree; Gene expression profiling; Network; Rough sets; Soft computing

Indexed keywords

ACCURACY; BREAST CANCER; CANCER CLASSIFICATION; COLON CANCER; COMPUTER PROGRAM; DOWN REGULATION; GENE EXPRESSION PROFILING; GENE FUNCTION; GENE INTERACTION; GENE REGULATORY NETWORK; HUMAN GENOME; LEARNING ALGORITHM; MACHINE LEARNING; NOTE; ROUGH SET; SERIAL ANALYSIS OF GENE EXPRESSION; UPREGULATION;

EID: 77955476232     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: None     Document Type: Note

References (35)

1. Ziarko W. Variable precision rough set model. J Comput Syst Sci. 1993; 46(1):39-59.
2. Wang X, Gotoh O. Microarray-based cancer prediction using soft computing approach. Cancer Inform. 2009;7:123-39.
3. Wang X, Gotoh O. Accurate molecular classification of cancer using simple rules. BMC Med Genomics. 2009;2:64.
4. Wang X, Gotoh O. A robust gene selection method for microarray-based cancer classification. Cancer Inform. 2010;9:15-30.
5. Wang X, Gotoh O. Inference of cancer-specific gene regulatory networks using soft computing rules. Gene Regul Syst Biol. 2010;4:19-34.
6. van't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530-6.
7. Wang J, Wen S, Symmans WF, Pusztai L, Coombes KR. The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data. Cancer Inform. 2009;7:199-216.
8. Pawlak Z. Rough set theory. International J. of Information and Computer Science. 1982;11:341-56.
9. Thangavela K, Pethalakshmib A. Dimensionality reduction based on rough set theory: A review. Appl Soft Comp. 2008;9(1):1-12.
10. Zhang X, Fournier MV, Ware JL, Bissell MJ, Yacoub A, Zehner ZE. Inhibition of vimentin or beta1 integrin reverts morphology of prostate tumor cells grown in laminin-rich extracellular matrix gels and reduces tumor growth in vivo. Mol Cancer Ther. 2009;(3):499-508.
11. Grate LR. Many accurate small-discriminatory feature subsets exist in microarray transcript data: biomarker discovery. BMC Bioinformatics. 2005;6:97.
12. Fayyad UM, Irani KB. Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference of Artificial Intelligence: August 28-September 3 1993. Chambéry, France: Morgan Kaufmann; 1993. pp. 1022-7.
13. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, 11(1);2009.
14. Jiang W, Li X, Rao S, et al. Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements. BMC Syst Biol. 2008;2:72.
15. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.
16. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37(4):382-90.
17. Maathuis MH, Colombo D, Kalisch M, Bühlmann P. Predicting causal effects in large-scale systems from observational data. Nat Methods. 2010;7(4):247-8.
18. Xing B, van der Laan MJ. A causal inference approach for constructing transcriptional regulatory networks. Bioinformatics. 2005;21(21):4007-13.
19. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2.
20. Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448-9.
21. Abba MC, Sun H, Hawkins KA, et al. Breast cancer molecular signatures as determined by SAGE: correlation with lymph node status. Mol Cancer Res. 2007;5(9):1-10.
22. Lopez-Bergami P, Lau E, Ronai Z. Emerging roles of ATF2 and the dynamic AP1 network in cancer. Nat Rev Cancer. 2010 Jan;10(1):65-76.
23. Bhoumik A, Ronai Z. ATF2: a transcription factor that elicits oncogenic or tumor suppressor activities. Cell Cycle. 2008 Aug;7(15):2341-5.
24. Fraser CC. G protein-coupled receptor connectivity to NF-kappaB in inflammation and cancer. Int Rev Immunol. 2008;27(5):320-50.
25. Zheng H, Ji C, Gu S, et al. Cloning and characterization of a novel RNA polymerase II C-terminal domain phosphatase. Biochem Biophys Res Commun. 2005 Jun 17;331(4):1401-7.
26. Amaral PP, Dinger ME, Mercer TR, Mattick JS. The eukaryotic genome as an RNA machine. Science. 2008;319(5871):1787-9.
27. Zhang Y. ETS-FUSions networking, triggering and beyond. Genet Epigenet. 2010;3:1-4.
28. Zaghloul NA, Katsanis N. Functional modules, mutational load and human genetic disease. Trends Genet. 2010;26(4):168-76.
29. Bessarabova M, Kirillov E, Shi W, Bugrim A, Nikolsky Y, Nikolskaya T. Bimodal gene expression patterns in breast cancer. BMC Genomics. 2010; 11 Suppl 1:S8.
30. Ourfali O, Shlomi T, Ideker T, Ruppin E, Sharan R. SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics. 2007;23(13):i359-66.
31. Zhong W, Sternberg PW. Genome-wide prediction of C. elegans genetic interactions. Science. 2006;311(5766):1481-4.
32. Lee I, Lehner B, Crombie C, Wong W, Fraser AG, Marcotte EM. A single gene network accurately predicts phenotypic effects of gene perturbation in C. elegans. Nat Genet. 2008;40(2):181-8.
33. Yip KY, Alexander RP, Yan KK, Gerstein M. Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data. PLoS One. 2010;5(1):e8121.
34. Li J, Wong L. Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics. 2002;(5): 725-34.
35. Geva-Zatorsky N, Dekel E, Cohen AA, Danon T, Cohen L, Alon U. Protein dynamics in drug combinations: a linear superposition of individual drug responses. Cell. 2010;140(5):643-51.