Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles

Proceedings of the Pacific Symposium on Biocomputing 2006, PSB 2006

Volumn , Issue , 2006, Pages 559-571

  Imoto, Seiya ^a   Tamada, Yoshinor ^b   Araki, Hiromitsu ^c   Yasuda, Kaori ^c   Print, Cristin G ^f   Charnock Jones, Stephen D ^d   Sanders, Deborah ^d   Savoie, Christopher J ^c   Tashiro, Kousuke ^e   Kuhara, Satoru ^e   Miyano, Satoru ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b KYOTO UNIVERSITY   (Japan)

^c Gene Networks International   (Japan)

^d UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^e KYUSHU UNIVERSITY   (Japan)

^f UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORK MODELS; COMPUTATIONAL STRATEGY; DNA MICROARRAY DATA; DYNAMIC BAYESIAN NETWORKS; EXPRESSION PROFILE; FENOFIBRATE; GENE NETWORKS; HUMAN ENDOTHELIAL CELLS; NOVEL GENES; PHARMACOGENOMICS; TIME COURSE;

BAYESIAN NETWORKS; CHEMICAL COMPOUNDS; RNA;

GENE EXPRESSION;

FENOFIBRATE; PEROXISOME PROLIFERATOR ACTIVATED RECEPTOR ALPHA; RNA; SMALL INTERFERING RNA;

ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; BIOLOGY; DNA MICROARRAY; DRUG EFFECT; ENDOTHELIUM CELL; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METABOLISM; PHARMACOGENETICS; STATISTICS;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; ENDOTHELIAL CELLS; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMANS; MODELS, GENETIC; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PHARMACOGENETICS; PPAR ALPHA; PROCETOFEN; RNA; RNA, SMALL INTERFERING;

EID: 33746616957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. T. Akutsu et al, Pac. Symp. Biocomput., 4:17-28, 1999.
2. K. Basso et al, Nat. Genet, 37:382-390, 2005.
3. A. Bernard and A.J. Hartemink, Pac. Symp. Biocomput, 10:459-470, 2005.
4. A. Cabrera et at., Curr. Drug Targets Inflamm. Allergy, 1:243-248, 2002.
5. T. Chen et al., Pac. Symp. Biocomput., 4:29-40, 1999.
6. D. di Bernardo et al., Nat. Genet., 37:382-390, 2005.
7. N. Friedman et al., J. Comp, Biol, 7:601-620, 2000.
8. K. Goya et al., Arteriosckr. Thromb. Vase. Biol., 24:658-663, 2004.
9. A.J. Hartemink et al., Pac. Symp. Biocomput, 7:437-449, 2002.
10. K. Hayashida et al., Biochem. Biophys. Res. Commun., 323:1116-1123, 2004.
11. D. Heckerman et al., Machine Learning, 20:197-243, 1995.
12. S. Imoto et al., Pac. Symp. Biocomput, 7:175-186, 2002.
13. S. Imoto et al., J. Bioinform. Comp. Biol., 2:77-98, 2004.
14. S. Imoto et al., J. Bioinform. Comp. Biol., 1:459-474, 2003.
15. K.K. Islam et aL, Biochim. Biophys. Acta., 1734:259-268, 2005.
16. S. Kersten et al, FASEB J., 15:1971-1978, 2001.
17. S. Kim et al, Biosystems, 75:57-65, 2004.
18. T.I. Lee et al., Science, 298:799-804, 2002.
19. M.J. Marton et al., Nat. Med., 4:1293-1301, 1998.
20. C.J. Savoie et al, DNA Res., 10:19-25, 2003.
21. J.M. Shipley and D.J. Waxman, Mol. Pharmacol., 64:355-364, 2003.
22. Y. Tamada et al, Genome Informatics, 16:182-191, 2005.
23. E.P. van Someren et al, Pharmacogenomics, 3:507-525, 2002.