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Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

By Pingzhao Hu, Celia M.T. Greenwood and Joseph Beyene
Topics: Original Research
Publisher: Libertas Academica
OAI identifier: oai:pubmedcentral.nih.gov:2675508
Provided by: PubMed Central
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    Citations

    1. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.
    2. (2003). A molecular signature of metastasis in primary solid tumors.
    3. (2005). affyPLM: Fitting probe level models.
    4. (2001). Analysis of gene expression identifi es candidate markers and pharmacological targets in prostate cancer.
    5. (2004). Cancer genes and the pathways they control.
    6. (2005). Combining Affymetrix microarray results.
    7. (2003). Combining multiple microarray studies and modeling inter-study variation.
    8. (2002). Comparison of discrimination methods for the classifi cation of tumors using gene expression data.
    9. (2002). Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease.
    10. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing.
    11. (2002). Gene expression correlates of clinical prostate cancer behaviour. Cancer Cell,
    12. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.
    13. (2004). Gene expression profi ling identifi es clinically relevant subtypes of prostate cancer.
    14. (2000). Gene ontology: tool for the unifi cation of biology. The Gene Ontology Consortium.
    15. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profi les.
    16. (2004). In silico dissection of cell-type-associated patterns of gene expression in prostate cancer.
    17. (2006). Integrating Affymetrix microarray data sets using probe-level test statistic for predicting prostate cancer.
    18. (2005). Integrating probe-level expression changes across generations of Affymetrix arrays.
    19. (2005). Integrative analysis of multiple gene expression profi les with quality-adjusted effect size models.
    20. (2002). KEGG: Kyoto encyclopedia of genes and genomes.
    21. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
    22. (2004). Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization.
    23. (2006). Merging microarray data, robust feature selection, and predicting prognosis in prostate cancer.
    24. (2002). Meta-analysis of microarrays: inter-study validation of gene expression profi les reveals pathway dysregulation in prostate cancer.
    25. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
    26. (2005). Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data. Nucleic acids research,
    27. (2005). Multiplelaboratory comparison of microarray platforms.
    28. (2006). Nonparametric pathway-based regression models for analysis of genomic data. Biostatistics,
    29. (2005). Outcome signature genes in breast cancer: is there a unique set?
    30. (2003). PGC-lalpha-responsive genes involved in oxidative phosphorylation are coordinatively downregulated in human diabetes.
    31. (2005). Robust prostate cancer gene emerge from direct integration of interstudy microarray data.
    32. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data.
    33. (2005). Simple decision rules for classifying human cancers from gene expression profi les.
    34. (1998). Statistical learning theory.
    35. (2006). Statistical methods for meta-analysis of microarray data: a comparative study. Information Systems Frontiers,
    36. (1985). Statistical methods for meta-analysis.
    37. (2003). Summaries of Affymetrix GeneChip probe level data.
    38. (2004). The Gene Ontology (GO) database and informatics resource.

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