Current Bioinformatics Bibliography List 1-340.
June 18, 2002 .

1. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. [1]
2. Fundamental Patterns Underlying Gene Expression Profiles: Simplicity from Complexity. [2]
3. Interpreting Patterns of Gene Expression with Self-Organizing Maps: Methods and Application to Hematopoietic Differentiation. [3]
4. Supervised Harvesting of Expression Trees. [4]
5. Minireview: Gene Expression Data Analysis. [5]
6. Normalization Strategies for cDNA Microarrays. [6]
7. Generation of Patterns from Gene Expression Data by Assigning Confidence to Differentially Expressed Genes. [7]
8. Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments. [8]
9. Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines. [9]
10. Singular Value Decomposition for Genome-wide Expression Data Processing and Modeling. [10]
11. Coupled Two-way Clustering Analysis of Gene Microarray Data. [11]
12. Cluster Analysis and Display of Genome-wide Expression Patterns. [12]
13. Predicting Gene Regulatory Elements in Silico on a Genomic Scale. [13]
14. Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Array. [14]
15. Ongoing Immunoglobulin Somatic Mutation in Germinal Center B Cell-Like But Not in Activated B Cell-Like Diffuse Large Cell Lumphomas. [15]
16. Computational Methods for the Identification of Genes in Vertebrate Genomic Sequences. [16]
17. CLICK: A Clustering Algorithm for Gene Expression Analysis. [17]
18. Gene Expression Analysis with the Parametric Bootstrap. [18]
19. Dynamic Modeling of Gene Expression Data. [19]
20. Large-Scale Temporal Gene Expression Mapping of Central Nervous System Development. [20]
21. Gene Shaving as a Method for Identifying Distinct Sets of Genes with Similar Expression Patterns. [21]
22. Estimating the Posterior Probability of Differential Gene Expression from Microarray Data. [22]
23. Computational Methods for the Identification of Differential and Coordinated Gene Expression. [23]
24. The Sigificance of Digital Gene Expression Profiles. [24]
25. Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling. [25]
26. The Transcriptional Program of Sporulation in Budding Yeast. [26]
27. An Information-Intensive Approach to the Molecular Pharmacology of Cancer. [27]
28. Normalization for cDNA Microarry Data. [28]
29. Distinctive Gene Expression Patterns in Human Mammary Epithelial Cells and Breast Cancers. [29]
30. Large-Scale Statistical Analyses of Rice ESTs Reveal Correlated Patterns of Gene Expression. [30]
31. Prediction of Gene Function by Genome-Scale Expression Analysis: Prostate Cance-Associated Genes. [31]
32. Discriminant Analysis and Its Application in DNA Sequence Motif Recognition. [32]
33. CLIFF: Clustering of High-Dimensional Microarray Data via Iterative Feature Filtering Using Normalized Cuts. [33]
34. Tutorial: Gene Expression Data Analysis and Modeling. [34]
35. Shuffling Yeast Gene Expression Data. [35]
36. Optimal Arrangement of Leaves in the Tree Representing Hierarchical Clustering of Gene Expression Data. [36]
37. Gene Expression. [37]
38. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. [38]
39. Using Non-Parametric Methods in the Context of Multiple Testing to Identify Differentially Expressed Genes. [39]
40. Bayesian Classification of DNA Array Expression Data. [40]
41. Statistical Analysis of a Gene Expression Microarray Experiment with Replication. [41]
42. Bootstrapping Cluster Analysis: Assessing the Reliability of Conclusions from Microarray Experiments. [42]
43. Plaid Models for Gene Expression Data. [43]
44. Computational Analysis of Leukemia Microarray Expression Data Using the GA/KNN Method and Other Existing Tools. [44]
45. Zipf's Law in Importance of Genes for Cancer Classification Using Microarray Data. [45]
46. How Many Genes Are Needed for a Discriminant Microarray Data Analysis. [46]
47. From Features to Expression: High-Density Oligonucleotide Array Analysis Revisited. [47]
48. How Many Replicates of Arrays Are Required to Detect Gene Expression Changes in Microarray Experiments? a Mixture Modelq Approach. [48]
49. A Mixture Model Approach to Detecting Differentially Expressed Genes with Microarray Data. [49]
50. A Model for Measurement Error for Gene Expression Arrays. [50]
51. DNA Microarray Data Analysis and Regression Modeling for Genetic Expression Profiling. [51]
52. Validating Clustering for Gene Expression Data. [52]
53. Aligning Gene Expression Time Series with Time Warping Algorithms. [53]
54. A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-test and Statistical Inferences of Gene Changes. [54]
55. Context-Specific Bayesian Clustering for Gene Expression Data. [55]
56. Class Discovery in Gene Expression Data. [56]
57. Determining Significant Fold Differences in Gene Expression Analysis. [57]
58. A Hierarchical Unsupervised Growing Neural Network for Clustering Gene Expression Patterns. [58]
59. Extracting Information from cDNA Arrays. [59]
60. Leading the Way Using Microarray, A More Comprehensive Approach for Discovery of Gene Expression Patterns. [60]
61. Statistical Design and the Analysis of Gene Expression Microarrays. [61]
62. Experimental Design for Gene Expression Microarrays. [62]
63. Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks. [63]
64. Unsupervised Learning from Complex Data: the Matrix Incision Tree Algorithm. [64]
65. Mutual Information Analysis as a Tool to Assess the Role of Aneuploidy in the Generation of Cancer-Associated Differential Gene Expression Patterns. [65]
66. Model-Based Analysis of Oligonucleotide Arrays: Expression Index Computation and Outlier Detection. [66]
67. Global Gene Expression Profiling in Escherichia Coli K12. [67]
68. Analysis of Temporal Gene Expression Profiles: Clustering by Simulated Annealing and Determining the Optimal Number of Clusters. [68]
69. Use of Keyword Hierarchies to Interpret Gene Expression Patterns. [69]
70. On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data. [70]
71. A Nonparametric Scoring Algorithm for Identifying Informative Genes from Microarray Data. [71]
72. Gene Functional Classification from Heterogeneous Data. [72]
73. Inferring Subnetworks from Perturbed Expression Profiles. [73]
74. Systematic Analysis of DNA Microarray Data: Ordering and Interpreting Patterns of Gene Expression. [74]
75. Rich Probabilistic Methods for Gene Expression. [75]
76. Experimental Annotation of the Human Genome Using Microarray Technology. [76]
77. Meeting Report: Making Sense of Microarrays. [77]
78. Percolation Clustering: A Novel Algorithm Applied to the Clustering of Gene Expression Patterns in Dictyostelium Development. [78]
79. A Relational Schema for Both Array-Based and Sage Gene Expression Expriments. [79]
80. An Efficient and Robust Statistical Modeling Approach to Discover Differentially Expressed Genes Using Genomic Expression Profiles. [80]
81. Missing Value Estimation Methods for DNA Microarrays. [81]
82. Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. [82]
83. Feature Selection for High-Dimensional Genomic Microarray Data. [83]
84. An Empirical Study On Principal Component Analysis for Clustering Gene Expression Data. [84]
85. Recursive Partitioning for Tumor Classification with Gene Expression Microarray Data. [85]
86. Pattern Recognition of Genomic Features with Microarrays: Site Typing of Mycobacterium Tuberculosis Strains. [86]
87. Tissue Classification with Gene Expression Profiles. [87]
88. Processing and Quality Control of DNA Array Hybridization Data. [88]
89. Mutual Information Relevance Networks: Functional Genomic Clustering Using Pairwise Entropy Measurements. [89]
90. Analysis of Gene Expression Microarrays for Phenotype Classification. [90]
91. Biclustering of Expression Data. [91]
92. Genetic Network Inference: from Co-Expression Clustering to Reverse Engineering. [92]
93. Using Bayesian Networks to Analyze Expression Dat. [93]
94. The Application of Shannon Entropy in the Identification of Putative Drug Targets. [94]
95. Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. [95]
96. Super-Paramagnetic Clustering of Yeast Gene Expression Profiles. [96]
97. Analysis of Variance for Gene Expression Microarray Data. [97]
98. Importance of Replication in Microarray Gene Expression Studies: Statistical Methods and Evidence from Repetitive cDNA Hybridizations. [98]
99. Sequence Variation in Genes and Genomic DNA: Methods for Large-Scale Analysis. [99]
100. Analysis of Molecular Profile Data Using Generative and Discriminative Methods. [100]
101. Principal Components Analysis to Summarize Microarray Experiments: Application to Sporulation Time Series. [101]
102. Bioinformatics Tools for Whole Genomes. [102]
103. Analyzing High-Density Oligonucleotide Gene Expression Array Data. [103]
104. A Gene Expression Database for the Molecular Pharmacology of Cancer. [104]
105. Genes, Themes and Microarrays - Using Information Retrieval for Large-Scale Gene Analysis. [105]
106. Class Prediction and Discovery Using Gene Expression Data. [106]
107. Making and Using DNA Microarrays: A Short Course at Cold Spring Harbor Laboratory. [107]
108. Usage: A Web-Based Approach Towards the Analysis of SAGE Data. [108]
109. Linear Modeling of Genetic Networks from Experimental Data. [109]
110. Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data. [110]
111. A Fuzzy Logic Approach to Analyzing Gene Expression Data. [111]
112. Cluster, Function and Promoter: Analysis of Yeast Expression Array. [112]
113. Analysis of Gene Expression Data with Pathway Scores. [113]
114. Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model. [114]
115. Identifying Gene Regulatory Networks from Experimental Data. [115]
116. Linear Modeling of mRNA Expression Levels During CNS Developement and Injury. [116]
117. Large-Scale Clustering of cDNA-Fingerprinting Data. [117]
118. Exploring Expression Data: Identification and Analysis of Coexpressed Genes. [118]
119. Gene Expression Profiling, Genetic Networks, and Cellular States: An Integrating Concept for Tumorigenesis and Drug Discovery. [119]
120. Algorithms for Choosing Differential Gene Expression Experiments. [120]
121. Genetic Network Analysis in Light of Massively Parallel Biological Data Acquisitio. [121]
122. Clustering Methods for the Analysis of DNA Microarray Data. [122]
123. Modeling Regulatory Networks with Weight Matrices. [123]
124. Large-Scale Gene Expression Data Analysis: A New Challenge to Computational Biologists. [124]
125. NetWork: An Interactive Interface to the Tools for Analysis of Genetic Network Structure and Dynamics. [125]
126. REVEAL, a General Reverse Engineering Algorithm for Inference of Genetic Network Architectures. [126]
127. Cluster Analysis and Data Visualization of Large-Scale Gene Expression Data. [127]
128. Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. [128]
129. Technology for Microarray Analysis of Gene Expression. [129]
130. Computational Aspects of Expression Data. [130]
131. Yeast Microarrays for Genome Wide Parallel Genetic and Gene Expression Analysis. [131]
132. Parallel Human Genome Analysis: Microarray-Based Expression Monitoring of 1000 Genes. [132]
133. Model-Based Clustering and Data Transformations for Gene Expression Data. [133]
134. Prediction and Uncertainty in the Analysis of Gene Expression Profiles. [134]
135. Tumor Classification by Partial Least Squares Using Microarray Gene Expression Data. [135]
136. Whole-Genome Expression Analysis: Challenges Beyond Clustering. [136]
137. Some Computational Issues in Cluster Analysis with No a piori Metric. [137]
138. Gene Expression Profiling in the Human Hypothalamus-Pituitary-Adrenal Axis and Full-Length cDNA Cloning. [138]
139. The Expression of Adipogenic Genes Is Decreased in Obesity and Diabetes Mellitus. [139]
140. DNA Microarray Analysis of Gene Expression in Response to Physiological and Genetic Changes That Affect Tryptophan Metabolism in Escherichia coli. [140]
141. Genome-Wide Study of Aging and Oxidative Stress Response in Drosophila Melanogaster. [141]
142. Genome-Wide Analysis of Developmental and Sex-Regulated Gene Expression Profiles in Caenorhabditis Elegans. [142]
143. Expression Profiling Reveals Fundamental Biological Differences in Acute Myeloid Leukemia with Isolated Trisomy 8 and Normal Cytogenetic. [143]
144. Analysis of Gene Expression Profiles in Normal and Neoplastic Ovarian Tissue Samples Identifies Candidate Molecular Markers of Epithelial Ovarian Cancer. [144]
145. High-Sensitivity Array Analysis of Gene Expression for the Early Detection of Disseminated Breast Tumor Cells in Peripheral Blood. [145]
146. Genome-Wide Expression Analysis Reveals Dysregulation of Myelination-Related Genes in Chronic Schizophrenia. [146]
147. Genome-Wide Gene Expression Profiles of the Developing Mouse Hippocampus. [147]
148. Genomic Binding Sites of the Yeast Cell-Cycle Transcription Factors SBF and MBF. [148]
149. Genetic Network Analysis - from the Bench to Computers and Back the Millennium End Version. [149]
150. Correspondence Analysis Applied to Microarray Data. [150]
151. The Ovarian Kaleidoscope Database: An Online Resource for the Ovarian Research Community. [151]
152. Robust Cluster Analysis of DNA Microarray Data: An Application of Nonparametric Correlation Dissimilarity. [152]
153. Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and a Bayesian Statistical Framework. [153]
154. Mining for Low-Abundance Transcripts in Microarray Data. [154]
155. Bootstrapping Cluster Analysis: Assessing the Reliability of Conclusions from Microarray Experiments. [155]
156. Detecting Differentially Expressed Genes in Multiple Tag Sampling Experiments: Comparative Evaluation of Statistical Tests. [156]
157. Microarrays, Empirical Bayes Methods, and False Discovery Rates. [157]
158. Exploratory Screening of Genes and Clusters from Microarray Experiments. [158]
159. Estimating the Number of Clusters in a Dataset Via Gap Statistic. [159]
160. Image Metrics in the Statistical Analysis of DNA Microarray Data. [160]
161. Statistical modeling of large microarray data sets to identify stimulus-response profiles. [161]
162. Predicting the Clinical Status of Human Breast Cancer by Using Gene Expression Profiles. [162]
163. Gene Expression Profiling of Clear Cell Renal Cell Carcinoma: Gene Identification and Prognostic Classification. [163]
164. Genome-Wide Expression Profiling of Mid-Gestation Placenta and Embryo Using a 15,000 Mouse Developmental cDNA Microarray. [164]
165. Gene Expression Patterns of Breast Carcinomas Distinguish Tumor Subclasses with Clinical Implications. [165]
166. DNA/DNA Hybridization to Microarrays Reveals Gene-Specific Differences Between Closely Related Microbial Genomes. [166]
167. Antisense DNAs As Multisite Genomic Modulators Identified by DNA Microarray. [167]
168. Host Microarray Analysis Reveals a Role for the Salmonella Response Regulator phoP in Human Macrophage Cell Death. [168]
169. A Whole-Genome Microarray Reveals Genetic Diversity Among Helicobacter Pylori Strains. [169]
170. Coordinated Plant Defense Responses in Arabidopsis Revealed by Microarray Analysis. [170]
171. Analysis of Topoisomerase Function in Bacterial Replication Fork Movement: Use of DNA Microarrays. [171]
172. Whole-Genome Expression Analysis of snfy/swi Mutants of Saccharomyces Cerevisiae. [172]
173. Gene Microarray Identification of Redox and Mitochondrial Elements That Control Resistance Or Sensitivity to Apoptosis. [173]
174. Extraocular Muscle Is Defined by a Fundamentally Distinct Gene Expression Profile. [174]
175. Analysis of Gene Expression During Myc Oncogene-Induced Lymphomagenesis in the Bursa of Fabricius. [175]
176. Hypoxia-Induced Gene Expression Profiling in the Euryoxic Fish Gillichthys Mirabilis. [176]
177. Discovering Functional Relationships Between RNA Expression and Chemotherapeutic Susceptibility Using Relevance Networks. [177]
178. Multiple Differences in Gene Expression in Regulatory V24JQ T Cells from Identical Twins Discordant for Type I Diabetes. [178]
179. Identification of Eukaryotic mRNAs That Are Translated At Reduced Cap Binding Complex eIF4F Concentrations Using a cDNA Microarray. [179]
180. Informatic Selection of a Neural Crest-Melanocyte cDNA Set for Microarray Analysis. [180]
181. Systematic Changes in Gene Expression Patterns Following Adaptive Evolution in Yeast. [181]
182. Fast Optimal Leaf Ordering for Hierarchical Clustering. [182]
183. Visualizing Associations Between Genome Sequences and Gene Expression Data Using Genome-Mean Expression Profiles. [183]
184. GEST: A Gene Expression Search Tool Based On A Novel Bayesian Similarity Metric. [184]
185. Feature Selection for DNA Methylation Based Cancer Classification. [185]
186. Separation of Samples into Their Constituents Using Gene Expression Data. [186]
187. Centralization: A New Method for the Normalization of Gene Expression Data. [187]
188. Molecular Classification of Multiple Tumor Types. [188]
189. A Classification of Tasks in Bioinformatics. [189]
190. Assessing the Accuracy of Prediction Algorithms for Classification: An Overview. [190]
191. Identifying Splits with Clear Separation: A New Class Discovery Method for Gene Expression Data. [191]
192. Microarray Analysis Reveals Previously Unknown Changes in Toxoplasma gondii-infected Human Cells. [192]
193. Genome-Wide Responses to Mitochondrial Dysfunction. [193]
194. New Components of A System for Phosphate Accumulation and Polyphosphate Metabolism in Saccharomyces Cerevisiae Revealed by Genomic Expression Analysis. [194]
195. Genomic Expression Responses to DNA-damaging Agents and the Regulatory Role of the Yeast ATR Homolog Mec1p. [195]
196. Sensitivity Issues in DNA Array-Based Expression Measurements and Performance of Nylon Microarrays for Small Samples. [196]
197. Issues in cDNA Microarray Analysis: Quality Filtering, Channel Normalization, Models of Variations and Assessment of Gene Effects. [197]
198. An Evaluation of the Performance of cDNA Microarrays for Detecting Changes in Global mRNA Expression. [198]
199. Gene Discovery Using Computational and Microarray Analysis of Transcription in the Drosophila Melanogaster Testis. [199]
200. Microarray Expression Profiling Identifies Genes with Altered Expression in HDL-Deficient Mice. [200]
201. Detecting Gene Copy Number Fluctuations in Tumor Cells by Microarray Analysis of Genomic Representations. [201]
202. Systematic Management and Analysis of Yeast Gene Expression Data. [202]
203. Systematic Analysis of DNA Microarray Data: Ordering and Interpreting Patterns of Gene Expression. [203]
204. Transcriptional Gene Expression Profiles of Colorectal Adenoma, Adenocarcinoma, and Normal Tissue Examined by Oligonucleotide Arrays. [204]
205. The New Direction in Bioinformatics: Integrative Data Mining for Genomics and Proteomics. [205]
206. Gene Expression Data Mining for Functional Genomics. [206]
207. A Gene Expression Database for the Molecular Pharmacology of Cancer. [207]
208. Machine Learning for Science: State of the Art and Future Prospects. [208]
209. A New Approach to Decoding Life: Systems Biology. [209]
210. The Transcriptional Program in the Response of Human Fibroblasts to Serum. [210]
211. Identifying Expressed Genes. [211]
212. Impact of Genomics on Drug Discovery and Clinical Medicine. [212]
213. Functional Genomics and Expression Profiling Be There or Be Square. [213]
214. Bioinformatics A User's Perspective. [214]
215. Gene Expression Profiling of Primary Breast Carcinomas Using Arrays of Candidate Genes. [215]
216. Identifying Marker Genes in Transcription Profiling Data Using a Mixture of Feature Relevance Experts. [216]
217. Changes in Global Gene Expression Patterns During Development and Maturation of the Rat Kidney. [217]
218. Comparative Genome-Scale Analysis of Gene Expression Profiles in T Cell Lymphoma Cells during Malignant Progression Using a Complementary DNA Microarray. [218]
219. Ulcerative Colitis and Crohn's Disease: Distinctive Gene Expression Profiles and Novel Susceptibility Candidate Genes. [219]
220. Expression Profiling of Renal Epithelial Neoplasms, A Method for Tumor Classification and Discovery of Diagnostic Molecular Markers. [220]
221. Analysis of Mucosal Gene Expression in Inflammatory Bowel Disease by Parallel Oligonucleotide Arrays. [221]
222. Differential Gene Expression Profiling in Human Brain Tumors. [222]
223. Argus A New Database System for Web-Based Analysis of Multiple Microarray Data Sets. [223]
224. Science, Medicine, and the Future DNA Microarrays in Medical Practice. [224]
225. Organ-Specific Molecular Classification of Primary Lung, Colon, and Ovarian Adenocarcinomas Using Gene Expression Profiles. [225]
226. Molecular Signatures of Sepsis, Multiorgan Gene Expression Profiles of Systemic Inflammation. [226]
227. A Statistical Method for Flagging Weak Spots Improves Normalization and Ratio Estimates in Microarrays. [227]
228. Identification and Classification of Differentially Expressed Genes in Renal Cell Carcinoma by Expression Profiling on a Global Human 31,500-Element cDNA Array. [228]
229. Diversity of Gene Expression in Adenocarcinoma of The Lung. [229]
230. Classification of Human Lung Carcinomas by Mrna Expression Profiling Reveals Distinct Adenocarcinoma Subclasses. [230]
231. Identification of Toxicologically Predictive Gene Sets Using Cdna Microarrays. [231]
232. Analysis Issues for Gene Expression Array Data. [232]
233. Microarray Techniques in Pathology: Tool Or Toy?. [233]
234. Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. [234]
235. Microarray Analysis of Drosophila Development During Metamorphosis. [235]
236. egulatory Networks Revealed by Transcriptional Profiling of Damaged Saccharomyces cerevisiae Cells: Rpn4 Links Base Excision Repair with Proteasomes. [236]
237. Integrating Naive Bayes Models and External Knowledge to Examine Copper and Iron Homeostasis in S. Cerevisiae. [237]
238. Assessing Clusters and Motifs from Gene Expression Data. [238]
239. Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network. [239]
240. A New Approach for Filtering Noise from High-Density Oligonucleotide Microarray Datasets. [240]
241. Genomic Computing. Explanatory Analysis of Plant Expression Profiling Data Using Machine Learning. [241]
242. The Microarray Explorer Tool for Data Mining of cDNA Microarrays: Application for the Mammary Gland. [242]
243. Statistical Evaluation of Differential Experssion on cDNA Nylon Arrays with Replicated Experiments. [243]
244. Global Analysis of Gene Expression in Pulmonary Fibrosis Reveals Distinct Programs Regulating Lung Inflammation and Fibrosis. [244]
245. Decoupled Evolution of Coding Region and Mrna Expression Patterns after Gene Duplication: Implications for The Neutralist-Selectionist Debate. [245]
246. Expression Profiling Reveals Distinct Sets of Genes Altered during Induction and Regression of Cardiac Hypertrophy. [246]
247. Molecular evolution of multiple recurrent cancers of the bladder. [247]
248. Microarrays under the Microscope. [248]
249. Statistical Prediction of Single-Stranded Regions in RNA Secondary Structure and Application to Predicting Effective Antisense Target Sites and Beyond. [249]
250. Microarray Analysis of Trophoblast Differentiation: Gene Expression Reprogramming in Key Gene Function Categories. [250]
251. Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes. [251]
252. A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. [252]
253. Systematic Variation in Gene Expression Patterns in Human Cancer Cell Lines. [253]
254. Gene-Expression Profiles in Hereditary Breast Cancer. [254]
255. Comparative Hybridization of an Array of 21500 Ovarian cDNAs for the Discovery of Genes Overexpressed in Ovarian Carcinomas. [255]
256. Comparison of the Complete Protein Sets of Worm and Yeast: Orthology and Divergence. [256]
257. Protein Microarrays for Highly Parallel Detection and Quantitation of Specific Proteins and Antibodies in Complex Solutions. [257]
258. Promoter-Specific Binding of Rap1 Revealed by Genome-wide Maps of Protein-DNA Association. [258]
259. Genome-wide Characterization of the Zap1p Zinc-Responsive Regulon in Yeast. [259]
260. Molecular Portraits of Human Breast Tumours. [260]
261. A Global Profile of Germline Gene Expression in C. elegans. [261]
262. Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis. [262]
263. Stereotyped and Specific Gene Expression Programs in Human Innate Immune Responses to Bacteria. [263]
264. Comparative Gene Expression Profiles Following UV Exposure in Wild-Type and SOS-Deficient Escherichia coli. [264]
265. Identification of the Copper Regulon in Saccharomyces cerevisiae by DNA Microarrays. [265]
266. Global Analysis of Growth Phase Responsive Gene Expression and Regulation of Antibiotic Biosynthetic Pathways in Streptomyces Coelicolor Using Dna Microarrays. [266]
267. Global and Specific Translational Regulation in the Genomic Response of Saccharomyces cerevisiae to a Rapid Transfer from a Fermentable to a Nonfermentable Carbon Source. [267]
268. Microarray Analysis of Diurnal and Circadian-Regulated Genes in Arabidopsis. [268]
269. Two Yeast Forkhead Genes Regulate the Cell Cycle and Pseudohyphal Growth. [269]
270. Large-Scale Identification of Secreted and Membrane-Associated Gene Products Using DNA Microarrays. [270]
271. Genome-Wide Analysis of DNA Copy-Number Changes Using cDNA Microarrays. [271]
272. The Human Adult Skeletal Muscle Transcriptional Profile Reconstructed by a Novel Computational Approach. [272]
273. Identification of Genes Periodically Expressed in the Human Cell Cycle and Their Expression in Tumors. [273]
274. Relation of Gene Expression Phenotype to Immunoglobulin Mutation Genotype in B Cell Chronic Lymphocytic Leukemia. [274]
275. Finding Genes in the C2C12 Osteogenic Pathway by k-Nearest-Neighbor Classification of Expression Data. [275]
276. Analysis of DNA Microarrays Using Algorithms That Employ Rule-Based Expert Knowledge. [276]
277. Adjustments and Measures of Differential Expression for Microarray Data. [277]
278. Mixture Modelling of Gene Expression Data from Microarray Expreiments. [278]
279. Assessing the Significance of Consistently Mis-Regulated Genes in Cancer Associated Gene Expression Matrices. [279]
280. Analysis of matched mRNA measurements from two different microarray technologies. [280]
281. Microarray Data Warehouse Allowing for Inclusion of Experiment Annotations in Statistical Analysis. [281]
282. Linear Modes of Gene Expression Determined by Independent Component Analysis. [282]
283. Extracting Transcriptional Events from Temporal Gene Expression Patterns During Dictyostelium Development. [283]
284. Selection Bias in Gene Extraction on the Basis of Microarray Gene-Expression Data. [284]
285. Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. [285]
286. Scoring Genes for Relevance. [286]
287. Cluster Analysis and its Applications to Gene Expression Data. [287]
288. Paired and Unpaired Comparison and Clustering with Gene Expression Data. [288]
289. A New Approach to Analyzing Gene Expression Time Series Data. [289]
290. Replicated Microarray Data. [290]
291. Clustering Gene Expression Patterns. [291]
292. Iterative Linear Regression by Sector: Renormalization of cDNA Microarray Data and Cluster Analysis Weighted by Cross Homology. [292]
293. Cutting-edge Technology I. Global Gene Expression Profiling Using Dna Microarrays. [293]
294. A Clustering Method for Discovering Patterns Using Gene Regulatory Processes. [294]
295. Genome-scale Gene Expression Analysis and Pathway Reconstruction in KEGG. [295]
296. Robust Model-Based Clustering of Genes in Microarray Data: Are there Gene Clusters?. [296]
297. Multivariate approach for selecting sets of differentially Expressioned Genes. [297]
298. Analysis of Expression Patterns: The Scope of the Problem, the Problem of Scope. [298]
299. Sources of Variability and Effect of Experimental Approach on Expression Profiling Data Interpretation. [299]
300. Supplementary Information for: Diffuse Large B-Cell Lymphoma Outcome Prediction by Gene Expression Profiling and Supervised Machine Learning. [300]
301. Molecular Classification of Cutaneous Malignant Melanoma by Gene Expression Profiling. [301]
302. Statistical Issues in The Clustering of Gene Expression Data. [302]
303. Multi-Class Cancer Classfication Via Partial Least Squares With Gene Expression Profiles. [303]
304. Data Mining and Machine Learning Methods for Microarray Analysis. [304]
305. How to Use Boosting for Tumor Classification with Gene Expression Data. [305]
306. ANOVA Analysis of cDNA Microarray Data to Identify Differentially Expressed Genes. [306]
307. Statistical Intelligence: Effective Analysis of High-Density Microarray Data. [307]
308. Analysis of Gene Expression Profiles: Class Discovery and Leaf Ordering. [308]
309. Unsupervised Feature Selection in Gene Expression Analysis: Bootstrap Via Two-Way Ordering. [309]
310. Comparison of Discrimination Methods for the Classi cation of Tumors Using Gene Expression Data. [310]
311. Normalization for cDNA Microarray Data: A Robust Composite Method Addressing Single and Multiple Slide Systematic Variation. [311]
312. Tumour Class Prediction and Discovery by Microarray-Based DNA Methylation Analysis. [312]
313. Classification of Genes Using Probabilistic Models of Microarray Expression Profiles. [313]
314. Editorial: DNA Microarrays: Boundless Technology or Bound By Technology? Guidelines for Studies Using Microarray Technology. [314]
315. Vector Algebra in the Analysis of Genome-Wide Expression Data. [315]
316. Singular Value Decomposition Regression Models for Classification of Tumors from Microarray Experiments. [316]
317. An Algorithm for Clustering cDNAs for Gene Expression Analysis. [317]
318. KDD Cup 2001 Report. [318]
319. Statistical Inference for Simultaneous Clustering for Gene Expression Data. [319]
320. Mining Microarray Expression Data for Classifier Gene-Cores. [320]
321. The Bioinformatics of Microarray Gene Expression Profiling. [321]
322. CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis. [322]
323. Class Prediction and Discovery based on Gene Expression Data. [323]
324. Microarrays and Their Use in a Comparative Experiment. [324]
325. Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression. [325]
326. Adaptive Quality-Based Clustering of Gene Expression Profiles. [326]
327. A Model for Measurement Error for Gene Expression Arrays. [327]
328. Exploring Gene Expression Data With Class Scores. [328]
329. Hierarchical Cluster Analysis of Sage Data for Cancer Profiling. [329]
330. A Statistical Framework for Expression Based Molecular Classification in Cancer. [330]
331. Recursive Sample Classification and Gene Selection Based on SVM: Method and Software Description. [331]
332. DNA Microarray Normalisation, PCA and a Related Latent Variable Model. [332]
333. Microarrays: How Many Do You Need?. [333]
334. Permutation-Validated Principal Components Analysis of Microarray Data. [334]
335. Empirical Characterization of The Expression Ratio Noise Structure in High-Density Oligonucleotide Arrays. [335]
336. Model-Based Cluster Analysis of Microarray Gene-Expression Data. [336]
337. New Feature Subset Selection Procedures for Classification of Expression Profiles. [337]
338. Details of the Adjusted Rand index and Clustering algorithms Supplement to the paper An empirical study on Principal Component Analysis for clustering gene expression data. [338]
339. Identification of Regulatory Elements Using a Feature Selection Method. [339]
340. Principal Component Analysis for Clustering Gene Expression Data. [340]
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