We present a new approach for the evaluation of gene expression data. The basic idea is to generate biologically possible pathways and to score them with respect to gene expression measurements. We suggest sample scoring functions for di erent problem speci cations. We assess the signi cance of the scores for the investigated pathways by comparison to a number of scores for random pathways. We show that simple scoring functions can assign statistically signi cant scores to biologically relevant pathways. This suggests that the combination of appropriate scoring functions with the systematic generation of pathways can be used in order to select the most interesting pathways based on gene expression measurements.
|
798
|
Cluster analysis and display of genome-wide expression patterns
– Eisen, Spellman, et al.
- 1998
|
|
511
|
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
– Goloub, Slonim, et al.
- 1999
|
|
336
|
Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell
– SPELLMAN, SHERLOCK, et al.
- 1998
|
|
315
|
D: Using Bayesian networks to analyze expression data
– Friedman, Linial, et al.
|
|
295
|
Exploring the metabolic and genetic control of gene expression on a genomic scale, Science
– DeRisi, Iver, et al.
- 1997
|
|
270
|
Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation
– Tamayo, Slonim, et al.
- 1999
|
|
219
|
Knowledge-based analysis of microarray gene expression data by using support vector machines
– Brown, Grundy, et al.
- 2000
|
|
193
|
A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae
– Uetz, Giot, et al.
- 2000
|
|
139
|
The transcriptional program of sporulation in budding yeast. Science 282: 699–705
– Chu, DeRisi, et al.
- 1998
|
|
125
|
monitoring by hybridization to high-density oligonucleotide arrays, Nature Biotechnology
– Lockhart, Dong, et al.
- 1996
|
|
103
|
R: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput
– Liang, Fuhrman, et al.
|
|
96
|
A combined algorithm for genome-wide prediction of protein function
– Marcotte, Pellegrini, et al.
- 1999
|
|
86
|
Linear modeling of mRNA expression levels during CNS development and injury
– D’Haeseleer, Wen, et al.
- 1999
|
|
61
|
Principal components analysis to summarize microarray experiments: application to sporulation time series
– Raychaudhuri, JM, et al.
|
|
38
|
The ENZYME data bank
– Bairoch
- 1993
|
|
32
|
KEGG: kyoto encyclopedia of genes and genomes
– Ogata, Goto, et al.
- 1999
|
|
30
|
Accessing genetic information with high-density DNA arrays. Science 274:610–614
– Chee, Yang, et al.
- 1996
|
|
30
|
Pathway analysis in metabolic databases via differential metabolic display (DMD)” Bioinformatics 16
– Küffner, Zimmer, et al.
- 2000
|
|
26
|
Cluster analysis and data visualization of large-scale gene expression data
– Michaels, Carr, et al.
- 1998
|
|
19
|
From coexpression to coregulation: An approach to inferring transcriptional regulation amd gene classes from large-scale expression data
– Mjolsness, Mann, et al.
|
|
14
|
Molecular classi cation of cancer: class discovery and class prediction by gene expression monitoring
– Golub, Slonim, et al.
- 1999
|
|
14
|
Comprehensive identi� cation of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization
– Spellman, Sherlock, et al.
- 1998
|
|
12
|
Discovery and analysis of inflammatory disease-related genes using cDNA microarrays
– Heller, Schena, et al.
- 1997
|
|
10
|
DNA chips: promising toys have become powerful tools. TIBS
– Gerhold, Rushmore, et al.
- 1999
|
|
10
|
Large scale cDNA sequencing for analysis of quantitative and qualitative aspects of gene expression
– Okubo, Hori, et al.
- 1992
|
|
4
|
EST Databases as Multi-Conditional Gene Expression Datasets
– Ewing, Claverie
- 2000
|
|
3
|
Templates for Looking at Gene Expression Clustering. Statistical Computing & Statistical Graphics Newsletter 8(1):20{29
– Carr, Somogyi, et al.
- 1997
|
|
3
|
DNA chips: State-of-the art. Nature Biotechnology 16:40{44
– Ramsay
- 1998
|
|
2
|
Interpreting Clusters of Gene Expression Pro les in Terms of Metabolic Pathways
– Fellenberg, Mewes
- 1999
|
|
2
|
Discovery and analysis of in ammatory disease-related genes using cDNA microarrays
– Heller, Schena, et al.
- 1997
|
|
2
|
Complementary DNA sequence (EST) collections and the expression information of the human genome
– Okubo, Matsubara
- 1997
|
|
1
|
A comprehensive analysis of protein-protein interactions in saccharomyces cerevisiae. Nature 403(6770):623{631
– Srinivasan, Pochart, et al.
- 2000
|
|
1
|
Tantalizing Transcriptomes { SAGE and Its Use in Global Gene Expression Analysis. Science 286:1491{1492
– Velculescu
- 1999
|