Using Bayesian networks to analyze expression data

Journal of Computational Biology

Volumn 7, Issue 3-4, 2000, Pages 601-620

  Friedman, N ^a   Linial, M ^a   Nachman, I ^a   Pe'er, D ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Bayesian methods; Gene expression; Microarrays

Indexed keywords

FUNGAL DNA;

BAYES THEOREM; CONFERENCE PAPER; DNA DETERMINATION; DNA HYBRIDIZATION; GENE EXPRESSION; NONHUMAN; PRIORITY JOURNAL; SACCHAROMYCES CEREVISIAE; STATISTICAL ANALYSIS;

ALGORITHMS; BAYES THEOREM; CELL CYCLE; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENES, FUNGAL; MARKOV CHAINS; MODELS, GENETIC; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SACCHAROMYCES CEREVISIAE;

SACCHAROMYCES CEREVISIAE;

EID: 0033707946     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/106652700750050961     Document Type: Conference Paper

References (52)

1. Identification of gene regulatory networks by strategic gene disruptions and gene over-expressions
2. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
3. Gapped blast and psi-blast: A new generation of protein database search programs
4. Clustering gene expression patterns
5. Gene clusters and polycistronic transcription in eukaryotes
6. Theory refinement on Bayesian networks
7. Identifying gene regulatory networks from experimental data
8. A transformational characterization of equivalent Bayesian network structures
9. Learning Bayesian networks is NP-complete
10. A Bayesian method for the induction of probabilistic networks from data
11. Causal discovery from a mixture of experimental and observational data
12. Yeast G1 cyclins CLN1 and CLN2 and a GAP-like protein have a role in bud formation
13. Exploring the metabolic and genetic control of gene expression on a genomic scale
14. An impaired RNA polymerase II activity in Saccharomyces cerevisiae causes cell-cycle inhibition at START
15. Unraveling the role of helicases in transcription
16. Cluster analysis and display of genome-wide expression patterns
17. Data analysis with Bayesian networks: A bootstrap approach
18. Being Bayesian about network structure
19. Learning the structure of dynamic probabilistic networks
20. Gaussian process networks
21. Learning Bayesian network structure from massive datasets: The 'sparse candidate' algorithm
22. On the sample complexity of learning Bayesian networks
23. Learning Gaussian networks
24. A direct link between sister chromatid cohesion and chromosome condensation revealed through the analysis of MCD1 in S. cerevisiae
25. A tutorial on learning with Bayesian networks
26. Learning Bayesian networks: A unification for discrete and Gaussian domains
27. Learning Bayesian networks: The combination of knowledge and statistical data
28. A Bayesian approach to causal discovery
29. Discovering structure in continuous variables using Bayesian networks
30. The transcriptional program in the response of human fibroblasts to serum
31. Graphical models for associations between variables, some of which are qualitative and some quantitative
32. DNA expression monitoring by hybridization of high density oligonucleotide arrays
33. Model selection and accounting for model uncertainty in graphical models using Occam's window
34. Bayesian graphical models for discrete data
35. DNA repair, DNA replication, and UV mutagenesis
36. Cluster analysis and data visualization for large scale gene expression data
37. A theory of inferred causation
38. The gene expression matrix: Towards the extraction of genetic network architectures
39. Pfam: Multiple sequence alignments and hmm-profiles of protein domains
40. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization
41. An algorithm for causal inference in the presence of latent variables and selection bias
42. Effect of DNA lesions on transcription elongation
43. Modeling regulatory networks with weight matrices
44. Large-scale temporal gene expression mapping of central nervous system development
45. Protomap-automated classification of all protein sequences: A hierarchy of protein families, and local maps of the protein space