Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae

Physiological Genomics

Volumn 2001, Issue 4, 2001, Pages 127-135

  Moler, E J ^a,b   Radisky, D C ^a   Mian, I S ^a  

^a LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^b NOVARTIS PHARMA AG   (Switzerland)

Author keywords

Bayesian networks; Copper and iron metabolism; Gene profile vectors; Molecular profile matrix; Naive Bayes model

Indexed keywords

COPPER; IRON;

ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; DNA MICROARRAY; FUNGAL GENE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HOMEOSTASIS; METABOLISM; SACCHAROMYCES CEREVISIAE;

BAYES THEOREM; COPPER; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, FUNGAL; GENES, FUNGAL; HOMEOSTASIS; IRON; MODELS, GENETIC; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SACCHAROMYCES CEREVISIAE;

EID: 0034684814     PISSN: 15312267     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (31)

1. Aach J, Rindone W, and Church GM. Systematic management and analysis of yeast gene expression data. Genome Res 10: 431-445, 2000.
2. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, and Levine AJ. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 96: 6745-6750, 1999.
3. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M, Jr, and Haussler D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 97: 262-267, 2000.
4. Cheeseman P and Stutz J. Bayesian classification (Auto-Class): theory and results. In: Advances in Knowledge Discovery and Data Mining, edited by Fayyad UM, Piatetsky-Shapiro G, Smyth P, and Uthurusamy R. AAAI Press/MIT Press, 1996. [The software is available at http://ic-www.arc.nasa.gov/ic/projects/bayes-group/autoclass/index.html]
5. D'Haeseleer P, Wen X, Fuhrman S, and Somogyi R. Linear modeling of mRNA expression levels during CNS development and injury. In: Pacific Symposium on Biocomputing, 1999, p. 41-52.
6. Eisen MB, Spellman PT, Brown PO, and Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863-14868, 1998.
7. Friedman N, Linial M, Nachman I, and Pe'er D. Using Bayesian networks to analyze expression data [Online]. Stanford University. http://robotics.stanford.edu/people/nir/publications. html [2000].
8. Goss PJ and Peccoud J. Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. Proc Natl Acad Sci USA 95: 6750-6755, 1998.
9. Hastie T, Tibshirani R, Eisen M, Brown P, Ross D, Scherf U, Weinstein J, Alizadeh A, Staudt L, and Botstein D. Gene shaving: a new class of clustering methods for expression arrays [Online]. Stanford University. http://www-stat.stanford.edu/~hastie/Papers/[2000].
10. Hodges PE, McKee AH, Davis BP, Payne WE, and Garrels JI. The Yeast Proteome Database (YPD): a model for the organization and presentation of genome-wide functional data. Nucleic Acids Res. 27: 69-73, 1999. [The database is available at http://www.proteome.com/databases/index.html]
11. Jensen VF. An introduction to Bayesian Networks. London: UCL, 1996.
12. Jordan MI (editor). Learning in Graphical Models. Dordrecht, Netherlands: Kluwer Academic, 1998.
13. Kauffman S. The Origins of Order. Self-Organization and Selection in Evolution. Oxford: Oxford University Press, 1993.
14. Liang S, Fuhrman S, and Somogyi R. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In: Pacific Symposium on Biocomputing, 1998, p. 18-29.
15. Matsuno H, Doi A, Nagasaki M, and Miyano S. Hybrid Petri net representation of gene regulatory network. In: Pacific Symposium on Biocomputing, 2000, vol. 5, p. 338-349.
16. Mewes HW, Heumann K, Kaps A, Mayer K, Pfeiffer F, Stocker S, and Frishman D. MIPS: a database for genomes and protein sequences. Nucleic Acids Res 27: 44-48, 1999.
17. Moler EJ, Chow ML, and Mian IS. Analysis of molecular profile data using generative and discriminative methods. Physiol Genomics 4: 109-126, 2000.
18. Murphy K and Mian IS. Modelling gene expression data using dynamic Bayesian networks [Online]. University of California, Berkeley. http://www.cs.berkeley.edu/~murphyk/publ.html [1999].
19. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
20. Radisky D and Kaplan J. Regulation of transition metal transport across the yeast plasma membrane. J Biol Chem 274: 4481-3384, 1999.
21. Raychaudhuri R, Stuart JM, and Altman RB. Principal components analysis to summarize microarray experiments: application to sporulation time series. In Pacific Symposium on Biocomputing, 2000, vol. 5, p. 452-463.
22. Sanchez JC, Schaller D, Ravier F, Golaz O, Jaccoud S, Belet M, Wilkins MR, James R, Deshusses J, and Hochstrasser D. Translationally controlled tumor protein: a protein identified in several nontumoral cells including erythrocytes. Electrophoresis 18: 150-155, 1997.
23. Sjölander K, Karplus K, Brown M, Hughey R, Krogh A, Mian IS, and Haussler D. Dirichlet mixtures: a method for improving detection of weak but significant protein sequence homology. CABIOS 12: 327-345, 1996. [More up-to-date information is available at http://www.cse.ucsc.edu/research/compbio/dirichlets/index.html]
24. Somogyi R and Sniegoski CA. Modeling the complexity of genetic networks: understanding multigenetic and pleiotrophic regulation. Complexity 1: 45-63, 1996.
25. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, and Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9: 3273-3297, 1998. [The data are available at http://cellcycle-www.stanford.edu]
26. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, and Golub TR. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96: 2907-2912, 1999.
27. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, and Church GM. Systematic determination of genetic network architecture. Nat Gen 22: 281-285, 1999.
28. Toronen P, Kolehmainen M, Wong G, and Castren E. Analysis of gene expression data using self-organizing maps. FEBS Lett 451: 142-146, 1999.
29. Weaver DC, Workman CT, and Stormo GD. Modeling regulatory networks with weight matrices. In: Pacific Symposium on Biocomputing, 1999, p. 112-123.
30. Wen X, Fuhrman S, Michaels GS, Carr DB, Smith S, Barker JL, and Somogyi R. Large-scale temporal gene expression mapping of central nervous system development. Proc Natl Acad Sci USA 95: 334-339, 1998.
31. Yun C-Y, Ferea T, Rashford J, Ardon O, Brown PO, Botstein D, Kaplan J, and Philpott CC. Desferioxamine-mediated iron uptake in Saccharomyces cerevisiae: evidence for two pathways of iron uptake. J Biol Chem In press.