microarray
TECHNOLOGY 		expression
DATA 		data
ANALYSIS 		model
BUILDING 		selected
PUBLICATIONS




Selected PUBLICATIONS with respect to
A few other sites listing publications pertinent to microarray experiments and data analysis
EXPERIMENTS DATA ANALYSIS
CNS
Genetic Network Inference
S. Fuhrman et al.
Proceedings of the International Conference on Complex Systems, Nashua, N.H. (1997)

Large-scale Temporal Gene Expression Mapping of CNS Development
X. Wen et al.
Proc. Natl. Acad. Sci. USA 95:334-339 (1998)

Determination of Temporal Expression Patterns for Multiple Genes in the Rat Carotid Artery Injury Model
J.T.N. Tai et al.
Thromb. Vasc. Biol. 20: 2184-2191 (2000).
Templates for Looking at Gene Expression Clustering
D.B. Carr, R. Somogyi, and G. MIchaelis
Stat. Comput. & Stat. Graphics. Lett. April (1997)

Cluster Analysis and Data Visualization of Large-scale Gene Expression Data
G.S. Michaelis et al.
Pacific Symposium on Biocomputing '98, World Scientific Publishing (1998)

Mining the Gene Expression matrix: Inferring Gene Relationships from Large-scale Gene Expression Data
P. D'haeseleer et al.
Information Processing in Cells and Tissues, Holcombe and Paton (Eds.), Plenum Press, N.Y. (1998)

Linear Modeling of mRNA Expression Levels During CNS Development and Injury P. D'haeseleer et al.
Pacific Symposium on Biocomputing '99, World Scientific Publishing (1999)

Coarse-grained Reverse Engineering: Modelling Genomic Networks with Limited Data
J. Hertz and M. Wahde
Pacific Symposium on Biocomputing '99, Poster session on Gene Expression and Genetic Networks (1999)

Data Requirements for Inferring Genetic Networks from Expression Data
P. D'haeseleer
Pacific Symposium on Biocomputing '99, Poster session on Gene Expression and Genetic Networks (1999)

Coarse-grained Reverse Engineering of Genetic Regulatory Networks
M. Wahde and J. Hertz
BioSystems 55:129-136 (2000)

Clustering Gene EXpression Patterns
A. Ben-Dor, R. Shamir, and Z. Yakhini
J. Comput. Biol. 6:281-297 (1999)
YEAST TRANSCRIPTOME
Characterization of the Yeast Transcriptome
V.E. Velculescuet al.
Cell 88:243-251 (1997)
Yeast Microarrays for Genome Wide Parallel Genetic and Gene Expression Analsyis
D.A. Lashkari et al.
Proc. Natl. Acad. Sci. USA 94: 13057-13062 (1997)
Parallel Analysis of Genetic Selections Using Whole Genome Oligo Arrays
R.J. Cho et al.
Proc. Natl. Acad. Sci. USA 95:3752-3757 (1998)
Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale
J.L. DeRisi et al.
Science 278:680-686 (1997) 		Predicting Gene Regulatory Elements in silico on a Genomic Scale
A. Brazma et al.
Genome Res. 8:1202-1215 (1998)

Extracting Regulatory Sites from the Upstream Region of Yeast Genes by Computational Analysis of Oligo Frequencies
J. van Helden et al.
J. Mol. Biol. 281:827-842 (1998)

Cluster Analysis and Display of Genome-wide Expression Patterns
M.E. Eisen et al.
Proc. Natl. Acad. Sci. USA 95:14863-14868 (1998)

Analysis of Gene Expression data Using Self-organized Maps
P. Törönen et al.
FEBS Lett. 451:142-146 (1999)

Interpreting Clusters of Gene Expression Profiles in Terms of Metabolic Pathways
M. Fellenberg and H.-W. Mewes
GCB '99 Poster session

Mutual Information Relevance Networks: Functional Genomic Clustering Using Pairwise Entropy Measurements
A.J. Butte et al.
Pacific Symposium on Biocomputing 2000

Robustness Against Mutations in Genetic Networks of Yeast
A. Wagner
Nat. Genetics 24:355-361 (2000)

Gene Expression Data Analysis
A. Brazma and J. Vilo
FEBS Lett. 480:17-24 (2000)

Regulatory Element detection Using Correlation with Expression
H.J. Bussemaker, H. Li, and E.D. Siggia
Nat. Genetics 27:167 - 174 (2001)
Disecting the Regulatory Circuity of a Eukaryotic Genome
F.C.P. Holstege et al.
Cell 95:717-728 (1998)

Remodeling of Yeast Genome Expression in Response to Environmental Changes
H.C. Causton <et al.
Mol. Biol. Cell 12: 323-337 (2001)

Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle
I. Simon et al.
Cell 106:1-20 (2001)
Direct Allelic Variation Scanning of the Yeast Genome
E.A. Winzeler et al.
Science 281:1194-1196 (1998)
The Transcriptional Program of Sporulation in Budding Yeast
S. Chu et al.
Science 282:699-705 (1998) 		Principal Component Analysis to Summarize Microarray Experiments: Application to Sporulation Time-series
S. Raychaudhuri et al.
Pacific Symposium on Biocomputing 2000, in press

Cluster, Function and Promoter: Analysis of Yeast Expression Array
J. Zhu & M.Q. Zhang
Pacific Symposium on Biocomputing 2000

Fundamental Patterns Underlying Gene Expression Profiles: Simplicity from Complexity
N.S. Holter et al.
Proc. Natl. Acad. Sci. USA 97:8409-8414 (2000)

Bootstrapping Cluster Analysis: Assessing the Reliability of Conclusions from Microarray Experiments
M.K. Kerr & G.A. Churchill
PNAS, in press.

Dynamics Modeling of gene Expression Data
N.S. Holter et al.
Proc. Natl. Acad. Sci. USA 98:1693-1698 (2001)
Functional Characterization of S. cerevisiae Genome by Gene Deletion and Parallel Analysis
E.A. Winzeler et al.
Science 285:901-906 (1999)
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes
A.P. Gasch et al.
Mol. Biol. Cell 11:4241-4257 (2000)
New Components of a System for Phosphate accumulation and Polyphosphate Metabolism in S. cerevisiae Revealed by Genomic Expression Analysis
N. Ogawa et al.
Mol. Biol. Cell 11:4309-4321 (2000)
YEAST MITOTIC CELL CYCLE
Genome-wide Expression Monitoring in S. cerevisiae
L. Wodicka et al.
Nature Biotechnology 15:1359-1367 (1997)
A Genome-wide Transcriptional Analysis of the Mitotic Cell Cycle
R.J. Cho et al.
Mol. Cell 2:65-73 (1998) 		Interpreting Patterns of gene Expression with Self-organized Maps: Methods and Application to Hematopoietic Differentiation
P. Tamayo et al.
Proc. Natl. Acad. Sci. USA 96 2907-2912 (1999)

Systematic Determination of Genetic Network Architecture
S. Tavazoie et al.
Nature Gen. 22:281-285 (1999)

Exploring Expression Data: Identification and Analysis of Coexpressed Genes
L.J. Heyer et al.
Genome Research 9:1106-1115 (1999)

Assessing Clusters and Motifs from Gene Expression Data
L.M. Jakt et al.
Genome Research 11:112-123 (2000)

Statistical Modeling of Large Microarray data Sets to Identify Stimulus-response Profiles
L.P. Zhao, R. Prentice, and L. Breeden
PNAS 98: 5631-5636 (2001)
Comprehensive Identification of Cell Cycle-regulated Genes of Yeast S. cerevisiae by Microarray Hybridization
P.T. Spellman et al.
Mol. Biol. Cell 9:3273-3297 (1998)

A Large-scale Overexpression Screen in S. cerevisiae Identifies Previously Uncharacterized Cell Cycle Genes
L.F. Stevenson, B.K. Kennedy, and E. Harlow
Proc. Natl. Acad. SCi. USA 98: 3946-3951 (2001). 		Using Bayesian Networks to Analyze Whole-genome Expression Data: A Preliminary Investigation
N. Friedman et al.
HUJI Technical Report:CS99-6 (1998)

Candidate Regulatory Sequence Elements for Cell Cycle-dependent transcription in S. cerevisiae
T.G. Wolfsberg et al.
Genome Res. 9:775-792 (1999) (companion site)

Singular Value Decomposition for Genome-wide Expression Data Processing and Modeling
O. Alter, P.O. BRown, and D. Botstein
Proc. Natl. Acad. Sci. USA 97 10101-10106 (2001)

Statistical Modeling of Large Microarray data Sets to Identify Stimulus-response Profiles
L.P. Zhao, R. Prentice, and L. Breeden
PNAS 98: 5631-5636 (2001)
YEAST MUTANTS
Quantitative Phenotypic Analysis of Yeast Deletion Mutants Using a Highly Parallel Molecular Bar-coding Strategy
D.D. Shoemaker et al.
Nature Genetics 14:450-456 (1996)
Direct Allelic Variation Scanning of the Yeast Genome
E.A. Winzeler et al.
Science 281:1194-1197 (1998)
Systematic Changes in Gene Expression Patterns Following Adaptive Evolution in Yeast
T.R. Hughes et al.
Cell 102:109-126 (2000)
Functional Discovery via a Compendium of Expression Profiles
T.L. Ferea et al.
Proc. Natl. Acad. Sci. USA 96:9721-9726 (1999)
YEAST PROTEOME
Correlation Between Protein and mRNA Abundance in Yeast
S.P. Gygi et al.
Mol. Cell. Biol. 19:1720-1730 (1999)
A Sampling of the Yeast Genome
B. Futcher et al.
Mol. Cell. Biol. 19:7357-7368 (1999)
Measuring Gene Expression by Quantitative Proteome Analysis
S.P. Gygi, B. Rist, and R. Aebersold
Curr. Opin. Biotechnol. 11:396-401 (2000)
E. COLI
Quantitative Whole-genome Analysis of DNA-Protein Interactions by in vivo Methylase Protection in E. coli
S. Tavazoie and G. Church
Nature Biotechnology 16:566-571 (1998)
Genome-wide Expression Profiling in E. coli K-12
C.S. Richmond et al.
Nucl. Acid. Res. 27:3821-3835 (1999)
Prediction of Transcriptional Regulatory Sites in the Complete Genome Sequence of E. coli K-12
D. Thieffry et al.
Bioinformatics 14:391-400 (1999)
Functional Genomics: Expression Analysis of E. coli Growing on Minimal and Rich Media
H. Tao et al.
J. Bactriol. 181:6425-6440 (1999)
DNA Microarray Detection of Metabolic Responses to Protein Overproduction in E. coli
M.-K. Oh and J.C. Liao
Metabolic Eng. 2:201-209 (2000)
Nitrogen Regulatory Protein C-controlled Genes of E. coli: Savenging as a Defense Against Nitrogen Limitation
D.P. Zimmer et al.
Proc. Natl. Acad. Sci. USA 97:14674-14679 (2000)
RNA Expression Analysis Using a 30 Basepair Resolution E. coli Genome Array
D.W. Selinger et al.
Nature Biotechnology 18:1262-1268 (2000)
DNA Microarray Analysis of Gene Expression in Response to Physiological and Genetic Changes that Affect Tryptophan Metabolism in E. coli
A.B. Khodursky et al.
Proc. Natl. Acad. Sci. USA 97:12170-1275 (2000)
Gene Expression Profiling by DNA Microarrays and Metabolic Fluxes in E. coli
M.-K. Oh and J.C. Liao
Biotechnol. Prog. 16:278-286 (2000)
High-density Microarray-Mediated Gene Expression Profiling of E. coli
Y. Wei et al.
J. Bacteriol. 183:545-556 (2001)
Global Impact of sidA Amplification Revealed by Comprehensive Gene Expression Profiling of E. coli
Y. Wei et al.
J. Bacteriol. 183:2265-2272 (2001)
Robustness Analysis of the E. coli Metabolic Network
J.S. Edwards and B.O. Passon
Biotechnol. Prog. 16:927-939 (2000)
In silico Predictions of E. coli Metabolic Capabilities are Consistent with Experimental Data
J.S. Edwards, R.U. Ibarra, and B.O. Passon
Nature Biotechnology 19125-130 (2001)
Genomic Interspecies Microarray Hybridization: Rapid Discovery of Three Thoosand genes in the Maize Endophyte K. pneumoniae 342, by Microarray Hybridization with E. coli K-12 ORFs
Y. Dong et al.
Appl. Environ. Microbiol.
67: 19111-1921 (2001).
A Novel Application of gene Arrays: E. coli Array Provides Insight into the Biology of the Obligate Endosymbiont of Tetse Flies
L. Akman and S. Aksoy
Proc. Natl. Acad. Sci. USA 98: 7546-7551 (2001).
lobal Gene Expression Profiling in Escherichia coli K12. THE EFFECTS OF INTEGRATION HOST FACTOR
S.M. Arfin et al.
J. Biol. Chem. 275: 29672-29684 (2000). 		Improved Statistical Inference from DNA Microarray Data Uding Analysis of Varicne and a Bayesian Statistical Framework
A.D. Long et al.
J. Biol. Chem. 276: 19937-19944 (2001).
M. PNEUMONIAE
The Bacterium M. pneumoniae as a Model for a Minimal Self-replicating Cell
R. Herrmann et al.
ECCMIID '99 Genome Analysis and Infectious Diseases Session
Transcription in M. pneumoniae
J. Weiner III, R. Herrmann, and G.F. Browning
Nucleic Acids Res. 28: 4488-4496 (2000).
M. TUBERCULOSIS
Exploring Drug-induced Alterations in Gene Expression in M. tuberculosis by Microarray Hybridization
M. Wilson et al.
PNAS 96:12833-12838
CANCER-RELATED STUDIES
Distinctive Gene Expression Patterns in Human Mammary Epithelial Cells and Breast Cancers
C.M. Perou et al.
PNAS 96:9212-9217
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
T.R. Golub et al.
Science 286:531-537
Distinct Types of Diffuse Large B-cell Lymphoma Identified by Gene Expression Profiling
A.A. Alizadeh et al.
Nature 403:503-511
Signaling and Circuity of Multiple MAPK Pathways Revealed by a Matrix of Global Gene Expression Profiles
C.J. Roberts et al.
Science 287:873-880


PERSPECTIVES
01.- A Comparison of Selected mRNA and protein Abundances in Human Liver
L. Anderson and J. Seilhamer, Electrophoresis 18:533-537 (1997)
02.- Genetic Network Inference in Computational Models and Applications to Large-scale Gene Expression Data
R. Somogyi, Computational Methods in Molecular and Cellular Biology, CalTech, Pasadena, C.A. (1998)
03.- Gene Expression Analysis and Genetic Network Modeling
P. D'haeseleer et al., Pacific Symposium on Biocomputing '99, Tutorial session on Gene Expression and Genetic Networks (1999)
04.- The Chipping Forecast
nature Genetics 21:1-60 (1999) Supplement on on micro arrays, chips and what is possibly about to come
05.- Large-scale Gene Expression Data Analysis: Challenge to Computational Biologists
M.Q. Zhang, Genome Research 9:681-688 (1999)
06.- Making Sense of Gene-expression Data
R. Somogyi, Pharma INFORMATICS, Trends Supplement (1999)
07.- From Global Expression Data to Gene Networks
D. Thieffry, BioEssay 21:895-899 (1999)
08.- Learning to Think about Gene Expression Data
R. Brent, Current Biology 9:R338-341 (1999)
09.- Expression Profiling: DNA Arrays in many Guises
S.G. Granjeaud et al., BioEssays 21:781-790 (1999)
10.- From Global Expression Data to Gene Networks
D. Thieffry et al., BioEssay 21:895-899 (1999)
11.- Genome Prospecting: DIY gene Watching
Science 286:443 Genome Issue/News (1999)
12.- DNA-Chips - ein kurzer Überblick
F. Hänsel und H.P. Saluz, BIOforum 9:504-507 (1999)
13.- DNA Chips: Promising Toys Have Become Powerful Tools
D. Gerhold et al., TIBS 24-May (1999)
14.- Knowledge Discovery in Gene-expression Microarray data: Mining the Information Output of the genome
G. Zweiger, TIBTECH 17-November (1999)
15.- DNA Arrays for Analysis of Gene Expression
M.B. Eisen and P.O. Brown, Methods Enzymol. 303:179-205 (1999)
16.- Microarrays: their Origins and Applications
R. Ekins and F.W. Chu, TIBTECH 17-JUne:217-218 (1999)
17.- Genome Expression on the World Wide Web
E.G. Jennings and R.A. Young, Trends in Genetics 15-MAY (1999)
18.- Distribution and early Development of Microarray Technology in Europe
A. Vente et al., Nature genetics. 22:22-22 (1999)
19.- Messenger RNA Translation State: the Second Dimension of High-throughput Expression Screening
Q. Zong et al., Proc. Natl. Acad. Sci. USA 96:10632-10636 (1999)
20.- A Comparision of gel-based, Nylon Filter and Microarray techniques to Detect Differential RNA Expression in Plants
D. Baldwin, V. Crane, and D. Rice, Curr. Opin. Plant Biol. 2:96-103 (1999)
21.- Observing the Living Genome
T.L. Ferea and P. Brown, Curr. Opin. Gen. Develop. 9:715-722 (1999)
22.- Sensitivity Issues in DNA Array-based Expression Measurements and Performance of Nylon Microarrays for Small Samples
F. Bertucci et al., Hum. Mol. Gen. 8:1715-1722 (1999)
23.- One-stop Shop for Microarray Data
A. Brazma et al., Nature 403:699-700 (2000)
24.- Discovering Patterns in Microarray Data
H.B. Burke, Molecular Diagnosis 5:349-357 (2000)
25.- Making the most of Microarray Data
T. Gaasterland and S. Bekiranov, Nature genetics 24:204-206 (2000)
26.- Analysis of gene Expression by Microarrays: Cell Biologist's Gold Mine or Minefiled?
A. Schulze and J. Downward, J. Cell Science 113:4151-4156 (2000)
27.- Microarray Technology - Enhanced Versatility, Persistent Challenge
C.E. Epstein and R.A. Butlow, Curr. Opin. Biotech. 11:36-41 (2000)
28.- Assessment of the Sensitivity and Specificity of Oligo (50mer) Microarrays
M.D. Kane et al., Nuceleic Acids Res. 28:4552-4557 (2000)
29.- Transcription, Genomes, Function
R.J. Cho and M.J. Campbe, TRENDS in Genetics 16: 409-415 (2000)
30.- Of Microarrays and Meandering Data Points
S.R. Gullans, Nature genetics 26:4-5 (2000)
31.- Analysing uncharted transcriptomes with SAGE V.E. Velculescu et al., TIG 16: 423-425 (2000).
32.- Quantitative Assessment of DNA Microarray-Comparison with Northern Blot Analyses
M. Taniguchi et al., Genomics 71:34-39 (2001)
33.- Challenges in Bioinformatics: Infrastructure, Models, and Analytics
A.J. Butte, TRENDS in Biotechnology 19-MAY (2001)
34.- ChiPs with Everything
TRENDS in Genetics 17-MARCH (2001)
35.- An Evaluation of the Performance of cDNA Microarrays for Detecting Changes in Global mRNA Expression
H. Yue et al., Nuceleic Acids Res. 29:e41 (2001)
36.- Assessment of Clone Identity and Sequence Fidelity for 1189 IMAGE cDNA Clones
R.G. Halgren et al., Nuceleic Acids Res. 29:582-588 (2001)
37.- When Chips are Down
J. Knight, Nature 410:860-861 (2001)
38.- Translational Control: Briding the Gap between Genomics and Proteomics?
B. Pradet-Balade et al., TRENDS in Biochem. Sci. 26:225-229 (2001)
39.- Kinase Chips Hit the Proteomics Era
D.M. Williams and P.A. Cole, TRENDS in Biochem. Sci. 26:271-273 (2001)
40.- Identification and Prevention of a GC Content Bias in SAGE Libraries
E.H. Margulies, S.L.R. Kardia, and J.W. Innis, Nucleic Acids Res. 29: e60
41.- Microarrays: handling the deluge of data and extracting reliable information
K.R. Hess et al., Trends Biotechn. 19: 463
42.- Sources of nonlinearity in CDNA microarray expression measurements
L. Ramdas et al., Genome Biology 2: research0047.1


MODEL BUILDING
01.- Metabolic Stability and Epigenesis in Randomly Constructed Genetic Nets
S.A. Kauffman, J. theor. Biol. 22:437-467 (1969)
02.- The Logical Analysis of Continuous Nonlinear Biochemical Control Networks
L. Glass and S.A. Kauffman, J. theor. Biol. 39:103-129 (1973)
03.- Origins of Order: Self-organization and Selection in Evolution
Oxford University Press, Oxford (1987)
04.- Modeling the Complexity of Genetic Networks: Understanding Multigenic and Pleiotropic Regulation
R. Somogyi and C.A. Sniegoski, Complexity 1:45-63 (1996)
05.- NetWork: An Interactive Interface to the Tools for Analysis of Genetic Network Structure and Dynamics
M.G. Samsonova and V.N. Serov, Pac. Symp. Biocomput. 4:102-111 (1999)
06.- Modeling Regulatory Networks with Weight Matrices
D.C. Weaver et al., Pac. Symp. Biocomput. 4:112-123 (1999)
07.- Computational Studies of Gene Regulatory NetworksL in numero Molecular Biology
J. Hasty et al., Nat. Reviews Gen. 2: 268-279 (2001).


COMPUTATIONAL GENE EXPRESSION ANALYSIS
01.- Ratio-based Decisions and the Quantitative Analysis of cDNA Microarrays Images
Y. Chen et. al., J. Biomed. Opt. 2:364-374 (1997)
02.- The Significance of Digital Gene Expression Profiles
S. Audic & J.-M. Claverie, Genome Res. 7:986-995 (1997)
03.- Computational Aspects of Expression Data
M. Vingron & J. Hoheisel, J. Mol. Med. 77:3-7 (1999)
04.- Computational Methods for the Identification of differential and Coordinated Gene Expression
J.-M. Claverie, Hum. Mol. Gen. 8:1221-1832 (1999)
05.- Modeling Gene Expression with Differential Equations
T. Chen, H.L. He, and G.M. Church, Pacific Symp. Biocomp., URL (1999)
06.- Identifying Gene Regulatory Networks from Experimental Data
T. Chen, V. Filkov, S.S. Skiena, preprint, EMAIL
07.- Detecting Selective Expression of Genes and Proteins
L.D. Greller & F.L. Tobin, Genome Res. 9:282-296 (1999)
08.- Normalization Strategies for cDNA Microarray Experiments
J. Schuchhardt et al., Nucleic Acids Res. 28:e47 (2000)
09.- Normalization and Analysis of DNA Microarray Data by Self-consistency and Local Regression
T.B. Kepler, L. Crosby, and K.T. Morgan, Santa Fe Reserach Paper (2000)
10.- Testing for Differentially-expressed Genes by Maximum-likelihood Analysis of Microarray Data
T. Iyer et al., J. Comput. Biol. 7: 805-817 (2000)
11.- Statistical Modeling of genome-wide Expression Profiles: Identification of Differentially Expressed Genes Using DNA Microarrays
J.G. Thomas et al., Preprint (2000)
12.- How Many Genes are Needed for a Discriminant Microarray Data Analysis?
W. Li and Y. Yang, Talk (CAMDA), URL (2000)
13.- Analyzing High-density Olig1o gene Expression Array Data
E.E. Schadt et al., J. Cell. Biochem. 80:192-202 (2000)
14.- Processing and Quality Control of DNA Array Hybridization
T. Beißbarth et al., Bioinformatics 16:1014-1022 (2001)
15.- Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Experiments
S. Dudoit et al., Technical Report #578 AUGUST, URL, (2000)
16.- Reliability of Microarray Data and Clustering
D. Beule et al., Talk (GCB), URL (2000)
17.- Extracting Information from cDNA Arrays
H. Herzel et al., CHAOS 11:98-107 (2001)
18.- Model-based Analysis of Oligo Arrays: Expression Index Computation and Outlier Detection
C. Lim and W.H. Wong, Proc. atl. Acad. Sci. USA 98:31-36 (2001)
19.- Significance Analysis of Microarrays Applied to the Ionizing Radiation Response
V.G. Tusher, R. Tibshirani, and G. Chu, Proc. Natl. Acad. Sci. USA 98:5116-5121 (2001)
20.- Analysis of Variance for Gene Expression Microarray Data
M.K. Kerr, M. Martin, and G.A. Churchill, J. Comp. Biol. 7: 819-837 (2001).
21.- On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Chnages from Microarray Data
M.A. Newton et al., J. Comp. Biol. 8: 37-52 (2001).
22.- Unfolding of Microarray Data
A.B. Goryachev, P.F. Macgregor, and A.M. Edwards, J. Comp. Biol., in press, (2001)
23.- Computational Analysis of Microarray Data
J. Quackenbush, Nat. Reviews Gen. 2: 418-427 (2001).
24.- Statistical Design and the Analysis of Gene EXpression Microarray Data
M.K. Kerr & G.A. Churchill, genet. Res., Camb. 77: 123-128 (2001).
25.- A Literature Network of Human Genes for High-thtoughput Analysis of Gene Expression
T.-K. Jenssen et al., Nat. genetics 28:21-28 (2001).
26.- Experimental Design for Gene Expression Microarrays
M.K. Kerr & G.A. Churchill, Biostatistics, in press (2001).
27.- Determining Significant Fold Differences in Gene Expression Analysis
A.J. Butte et al., Pac. Symp. Biocomput. 6: 6-17 (2001).



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Last modified: Wed Nov 21 14:44:59 CET 2001

Study without reflection is a waste of time,
reflection without study is dangerous.  -Confucius