Literature DB >> 19698933

Analysis of DNA microarray expression data.

Richard Simon1.   

Abstract

DNA microarrays are powerful tools for studying biological mechanisms and for developing prognostic and predictive classifiers for identifying the patients who require treatment and are best candidates for specific treatments. Because microarrays produce so much data from each specimen, they offer great opportunities for discovery and great dangers or producing misleading claims. Microarray based studies require clear objectives for selecting cases and appropriate analysis methods. Effective analysis of microarray data, where the number of measured variables is orders of magnitude greater than the number of cases, requires specialized statistical methods which have recently been developed. Recent literature reviews indicate that serious problems of analysis exist a substantial proportion of publications. This manuscript attempts to provide a non-technical summary of the key principles of statistical design and analysis for studies that utilize microarray expression profiling.

Entities:  

Mesh:

Year:  2009        PMID: 19698933      PMCID: PMC2757654          DOI: 10.1016/j.beha.2009.07.001

Source DB:  PubMed          Journal:  Best Pract Res Clin Haematol        ISSN: 1521-6926            Impact factor:   3.020


  58 in total

1.  Sample size determination in microarray experiments for class comparison and prognostic classification.

Authors:  Kevin Dobbin; Richard Simon
Journal:  Biostatistics       Date:  2005-01       Impact factor: 5.899

Review 2.  When is a genomic classifier ready for prime time?

Authors:  Richard Simon
Journal:  Nat Clin Pract Oncol       Date:  2004-11

3.  Development and validation of therapeutically relevant multi-gene biomarker classifiers.

Authors:  Richard Simon
Journal:  J Natl Cancer Inst       Date:  2005-06-15       Impact factor: 13.506

4.  Prediction error estimation: a comparison of resampling methods.

Authors:  Annette M Molinaro; Richard Simon; Ruth M Pfeiffer
Journal:  Bioinformatics       Date:  2005-05-19       Impact factor: 6.937

5.  Sample size planning for developing classifiers using high-dimensional DNA microarray data.

Authors:  Kevin K Dobbin; Richard M Simon
Journal:  Biostatistics       Date:  2006-04-13       Impact factor: 5.899

6.  Use of genomic signatures in therapeutics development in oncology and other diseases.

Authors:  R Simon; S-J Wang
Journal:  Pharmacogenomics J       Date:  2006 May-Jun       Impact factor: 3.550

7.  Gene expression patterns and profile changes pre- and post-erlotinib treatment in patients with metastatic breast cancer.

Authors:  Sherry X Yang; Richard M Simon; Antoinette R Tan; Diana Nguyen; Sandra M Swain
Journal:  Clin Cancer Res       Date:  2005-09-01       Impact factor: 12.531

8.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

9.  Evolutionary algorithms for finding optimal gene sets in microarray prediction.

Authors:  J M Deutsch
Journal:  Bioinformatics       Date:  2003-01       Impact factor: 6.937

10.  A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.

Authors:  Carmen Lai; Marcel J T Reinders; Laura J van't Veer; Lodewyk F A Wessels
Journal:  BMC Bioinformatics       Date:  2006-05-02       Impact factor: 3.169

View more
  11 in total

Review 1.  Quality assurance of RNA expression profiling in clinical laboratories.

Authors:  Weihua Tang; Zhiyuan Hu; Hind Muallem; Margaret L Gulley
Journal:  J Mol Diagn       Date:  2011-10-20       Impact factor: 5.568

2.  Neutrophil chemotaxis and transcriptomics in term and preterm neonates.

Authors:  Steven L Raymond; Brittany J Mathias; Tyler J Murphy; Jaimar C Rincon; María Cecilia López; Ricardo Ungaro; Felix Ellett; Julianne Jorgensen; James L Wynn; Henry V Baker; Lyle L Moldawer; Daniel Irimia; Shawn D Larson
Journal:  Transl Res       Date:  2017-09-01       Impact factor: 7.012

3.  Poly(ADP-ribose) polymerase inhibition enhances p53-dependent and -independent DNA damage responses induced by DNA damaging agent.

Authors:  Diana Nguyen; Maria Zajac-Kaye; Larry Rubinstein; Donna Voeller; Joseph E Tomaszewski; Shivaani Kummar; Alice P Chen; Yves Pommier; James H Doroshow; Sherry X Yang
Journal:  Cell Cycle       Date:  2011-12-01       Impact factor: 4.534

4.  Discovery of molecular mechanisms of traditional Chinese medicinal formula Si-Wu-Tang using gene expression microarray and connectivity map.

Authors:  Zhining Wen; Zhijun Wang; Steven Wang; Ranadheer Ravula; Lun Yang; Jun Xu; Charles Wang; Zhong Zuo; Moses S S Chow; Leming Shi; Ying Huang
Journal:  PLoS One       Date:  2011-03-28       Impact factor: 3.240

5.  mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

Authors:  Hala Alshamlan; Ghada Badr; Yousef Alohali
Journal:  Biomed Res Int       Date:  2015-04-15       Impact factor: 3.411

6.  Radiation dose-rate effects on gene expression for human biodosimetry.

Authors:  Shanaz A Ghandhi; Lubomir B Smilenov; Carl D Elliston; Mashkura Chowdhury; Sally A Amundson
Journal:  BMC Med Genomics       Date:  2015-05-12       Impact factor: 3.063

7.  An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

Authors:  P M Booma; S Prabhakaran; R Dhanalakshmi
Journal:  ScientificWorldJournal       Date:  2014-06-16

8.  Clinical implementation of RNA signatures for pharmacogenomic decision-making.

Authors:  Weihua Tang; Zhiyuan Hu; Hind Muallem; Margaret L Gulley
Journal:  Pharmgenomics Pers Med       Date:  2011-09-08

9.  Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data.

Authors:  Shuaiqun Wang; Wei Kong; Weiming Zeng; Xiaomin Hong
Journal:  Biomed Res Int       Date:  2016-08-04       Impact factor: 3.411

10.  Unique transcriptomic response to sepsis is observed among patients of different age groups.

Authors:  Steven L Raymond; María Cecilia López; Henry V Baker; Shawn D Larson; Philip A Efron; Timothy E Sweeney; Purvesh Khatri; Lyle L Moldawer; James L Wynn
Journal:  PLoS One       Date:  2017-09-08       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.