The DOI matches its Crossref record; 97 of the 99 references it cites were found in a registry or an index.
Microarray data analysis: from disarray to consolidation and consensus
Abstract
In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The pooling strategy averages biological variations, increases statistical power, and reduces workload (Allison et al., 2006).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Allison et al., 2006
In the citing paper
“The pooling strategy averages biological variations, increases statistical power, and reduces workload (Allison et al., 2006).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Allison et al., 2006
In the citing paper
“The pooling strategy averages biological variations, increases statistical power, and reduces workload (Allison et al., 2006).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Allison et al., 2006
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,186
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,837
Yang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., & Speed, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.4.e15 · Cited by 3,722
The positive false discovery rate: a Bayesian interpretation and the q-value
Storey, J. D. (2003) · The Annals of Statistics · doi:10.1214/aos/1074290335 · Cited by 2,204
Microarray data normalization and transformation
Quackenbush, J. (2002) · Nature Genetics · doi:10.1038/ng1032 · Cited by 1,975
Flagged first, then by citations
2 of the 99 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Osier, M. V. in DNA Microarrays and Statistical Genomic Techniques: Design, Analysis, and Interpretation of Experiments (Marcel Dekker, New York, 2005).
- Dobbin, K. & Simon, R. Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 6, 27–38 (2005). · DOI
Cited by
Sorted by citations
Items 101–200 of 1,000
Detection and interpretation of expression quantitative trait loci (eQTL)
Michaelson, J. J., Loguercio, S., & Beyer, A. (2009) · Methods · doi:10.1016/j.ymeth.2009.03.004 · Cited by 140
Quality Assessment for Short Oligonucleotide Microarray Data
Brettschneider, J. A., Collin, F., Bolstad, B. M., & Speed, T. P. (2008) · Technometrics · doi:10.1198/004017008000000334 · Cited by 132
The Phenotype of Limbal Epithelial Stem Cells
Figueira, E. C., Girolamo, N. D., Coroneo, M. T., & Wakefield, D. (2006) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.06-0346 · Cited by 129
Edelman, L. B., Eddy, J. A., & Price, N. D. (2009) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.75 · Cited by 127
Is molecular profiling ready for use in clinical decision making?
Ioannidis, J. (2007) · The Oncologist · doi:10.1634/theoncologist.12-3-301 · Cited by 126
Swindell, W. R. (2006) · Genetics · doi:10.1534/genetics.106.061374 · Cited by 127
Using omics approaches to understand pulmonary diseases
Kan, M., Shumyatcher, M., & Himes, B. E. (2017) · Respiratory Research · doi:10.1186/s12931-017-0631-9 · Cited by 129
de Tayrac, M., Lê, S., Aubry, M., Mosser, J., & Husson, F. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-32 · Cited by 123
Computational and analytical framework for small RNA profiling by high-throughput sequencing
Fahlgren, N., Sullivan, C. M., Kasschau, K. D., Chapman, E. J., Cumbie, J. S., Montgomery, T. A., Gilbert, S. D., Dasenko, M., Backman, T. W. H., Givan, S. A., et al. (2009) · RNA · doi:10.1261/rna.1473809 · Cited by 127
Pelizzola, M., Koga, Y., Urban, A. E., Krauthammer, M. O., Weissman, S. M., Halaban, R., & Molinaro, A. M. (2008) · Genome Research · doi:10.1101/gr.080721.108 · Cited by 121
Pagmantidis, V., Méplan, C., van Schothorst, E. M., Keijer, J., & Hesketh, J. E. (2008) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/87.1.181 · Cited by 121
Gao, H., Yang, Z. K., Wu, L., Thompson, D. K., & Zhou, J. Z. (2006) · Journal of Bacteriology · doi:10.1128/jb.01908-05 · Cited by 120
Várkonyi, T., Nagy, B., Füle, T., Tarca, A. L., Karászi, K., Schönléber, J., Hupuczi, P., Mihalik, N., Kovalszky, I., Rigó, J. J., et al. (2010) · Placenta · doi:10.1016/j.placenta.2010.04.014 · Cited by 124
Kabani, S., Fenn, K., Ross, A., Ivens, A. C., Smith, T., Ghazal, P., & Matthews, K. R. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-427 · Cited by 120
Carrie, C., Giraud, E., Duncan, O., Lin, X., Wang, Y., Huang, S., Clifton, R., Murcha, M. W., Filipovska, A., Rackham, O., et al. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.121202 · Cited by 116
Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases
Emmert‐Streib, F., & Glazko, G. V. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002053 · Cited by 114
Keegan, K. P., Pradhan, S., Wang, J.-P., & Allada, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030208 · Cited by 117
quantro: a data-driven approach to guide the choice of an appropriate normalization method
Hicks, S. C., & Irizarry, R. A. (2015) · Genome biology · doi:10.1186/s13059-015-0679-0 · Cited by 117
Exon arrays provide accurate assessments of gene expression
Kapur, K., Xing, Y., Ouyang, Z., & Wong, W. H. (2007) · Genome biology · doi:10.1186/gb-2007-8-5-r82 · Cited by 116
Extracting Gene Networks for Low-Dose Radiation Using Graph Theoretical Algorithms
Voy, B. H., Scharff, J. A., Perkins, A., Saxton, A. M., Borate, B. R., Chesler, E. J., Branstetter, L. R., & Langston, M. A. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020089 · Cited by 111
The fibromatosis signature defines a robust stromal response in breast carcinoma
Beck, A. H., Espinosa, Í., Gilks, C. B., van de Rijn, M., & West, R. B. (2008) · Laboratory Investigation · doi:10.1038/labinvest.2008.31 · Cited by 107
D'Amore, S., Vacca, M., Cariello, M., Graziano, G., D'Orazio, A., Salvia, R., Sasso, R. C., Sabbà, C., Palasciano, G., & Moschetta, A. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2016.07.003 · Cited by 107
Mayday - integrative analytics for expression data
Battke, F., Symons, S., & Nieselt, K. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-121 · Cited by 108
Brucklacher, R. M., Patel, K. M., VanGuilder, H. D., Bixler, G. V., Barber, A. J., Antonetti, D. A., Lin, C., LaNoue, K. F., Gardner, T. W., Bronson, S. K., et al. (2008) · BMC Medical Genomics · doi:10.1186/1755-8794-1-26 · Cited by 110
Systems vaccinology: learning to compute the behavior of vaccine induced immunity
Nakaya, H. I., Li, S., & Pulendran, B. (2011) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.163 · Cited by 110
Global gene expression analysis of human erythroid progenitors
Merryweather‐Clarke, A. T., Atzberger, A., Soneji, S., Gray, N., Clark, K., Waugh, C., McGowan, S. J., Taylor, S., Nandi, A. K., Wood, W. G., et al. (2011) · Blood · doi:10.1182/blood-2010-07-290825 · Cited by 105
Validating the genomic signature of pediatric septic shock
Cvijanovich, N. Z., Shanley, T. P., Lin, R., Allen, G. L., Thomas, N. J., Checchia, P. A., ANAS, N. G., Freishtat, R. J., Monaco, M., Odoms, K., et al. (2008) · Physiological Genomics · doi:10.1152/physiolgenomics.00025.2008 · Cited by 109
Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets
Xiong, Q., Ancona, N., Hauser, E. R., Mukherjee, S., & Furey, T. S. (2012) · Genome Research · doi:10.1101/gr.124370.111 · Cited by 103
Alignment of LC‐MS images, with applications to biomarker discovery and protein identification
Vandenbogaert, M., Li‐Thiao‐Té, S., Kaltenbach, H., Zhang, R., Aittokallio, T. A., & Schwikowski, B. (2008) · PROTEOMICS · doi:10.1002/pmic.200700791 · Cited by 108
The Molecular Phenotype of Kidney Transplants
Halloran, P. F., de Freitas, D. G., Einecke, G., Famulski, K. S., Hidalgo, L. G., Mengel, M., REEVE, J. P., Sellares, J., & Sis, B. (2010) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2010.03267.x · Cited by 105
Multiple testing on the directed acyclic graph of gene ontology
Goeman, J. J., & Mansmann, U. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm628 · Cited by 105
An Eight-Gene Blood Expression Profile Predicts the Response to Infliximab in Rheumatoid Arthritis
Julià, A., Erra, A., Palacio, C., Tomás, C., Sans, X., Barceló, P., & Marsal, S. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007556 · Cited by 104
Medeiros, F., Rigl, C. T., Anderson, G. G., Becker, S. H., & Halling, K. C. (2007) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2007-131-1805-thfgea · Cited by 104
Test set bias affects reproducibility of gene signatures
Patil, P., Bachant-Winner, P.-O., Haibe‐Kains, B., & Leek, J. T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv157 · Cited by 102
Systems Biology and Mode of Action Based Risk Assessment
Edwards, S. W., & Preston, R. J. (2008) · Toxicological Sciences · doi:10.1093/toxsci/kfn190 · Cited by 103
Ecotoxicogenomics: Emerging Technologies for Emerging Contaminants1
Poynton, H. C., & Vulpe, C. D. (2009) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.2008.00291.x · Cited by 105
Biophysical properties of nucleic acids at surfaces relevant to microarray performance
Rao, A. N., & Grainger, D. W. (2014) · Biomaterials Science · doi:10.1039/c3bm60181a · Cited by 106
High-throughput screening for efficient microbial biotechnology
Sarnaik, A. P., Liu, A., Nielsen, D. R., & Varman, A. M. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.02.019 · Cited by 102
The Molecular Phenotype of Kidney Transplants: Insights From the MMDx Project
Halloran, P. F., Madill-Thomsen, K. S., & REEVE, J. P. (2023) · Transplantation · doi:10.1097/tp.0000000000004624 · Cited by 108
Prediction of Graft-Versus-Host Disease in Humans by Donor Gene-Expression Profiling
Baron, C., Somogyi, R., Greller, L. D., Rineau, V., Wilkinson, P. A., Cho, C. R., Cameron, M. J., Kelvin, D. J., Chagnon, P., Roy, D., et al. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040023 · Cited by 103
Rapid acid treatment of Escherichia coli: transcriptomic response and recovery
Kannan, G., Wilks, J. C., Fitzgerald, D. M., Jones, B. D., BonDurant, S. S., & Slonczewski, J. L. (2008) · BMC Microbiology · doi:10.1186/1471-2180-8-37 · Cited by 102
Reimand, J., Vaquerizas, J. M., Todd, A. E., Vilo, J., & Luscombe, N. M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq232 · Cited by 102
The application of systems biology to drug discovery
Cho, C. R., Labow, M., Reinhardt, M., van Oostrum, J., & Peitsch, M. C. (2006) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2006.06.025 · Cited by 101
Holmans, P. (2010) · Advances in genetics · doi:10.1016/b978-0-12-380862-2.00007-2 · Cited by 101
Data integration and predictive modeling methods for multi-omics datasets
Kim, M., & Tagkopoulos, I. (2017) · Molecular Omics · doi:10.1039/c7mo00051k · Cited by 106
Gene set selection via LASSO penalized regression (SLPR)
Frost, H. R., & Amos, C. I. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx291 · Cited by 102
Interpretation of microarray data in cancer
Michiels, S., Koscielny, S., & Hill, C. (2007) · British Journal of Cancer · doi:10.1038/sj.bjc.6603673 · Cited by 98
GeneTools – application for functional annotation and statistical hypothesis testing
Beisvåg, V., Jünge, F. K., Bergum, H., Jølsum, L., Lydersen, S., Günther, C., Ramampiaro, H., Langaas, M., Sandvik, A. K., & Lægreid, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-470 · Cited by 98
Baton, L. A., Robertson, A. E., Warr, E., Strand, M. R., & Dimopoulos, G. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-257 · Cited by 98
Celton, M., Malpertuy, A., Lelandais, G., & de Brevern, A. G. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-15 · Cited by 97
Gabrielsen, A., Lawler, P. R., Yongzhong, W., Steinbrüchel, D. A., Dimo, B., Paulsson‐Berne, G., Kastrup, J., & Hansson, G. K. (2007) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2006.12.016 · Cited by 96
Genes, behavior and next‐generation RNA sequencing
Hitzemann, R., Bottomly, D., Darakjian, P., Walter, N., Iancu, O., Searles, R., Wilmot, B., & McWeeney, S. (2013) · Genes, Brain and Behavior · doi:10.1111/gbb.12007 · Cited by 88
Identification of Pax6-Dependent Gene Regulatory Networks in the Mouse Lens
Wolf, L. V., Yang, Y., Wang, J., Xie, Q. M., Braunger, B. M., Tamm, E. R., Zavadil, J., & Cvekl, A. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004159 · Cited by 98
Acute lymphoblastic leukemia in adolescents and young adults in Finland
Usvasalo, A., Räty, R., Knuutila, S., Vettenranta, K. K., Harila‐Saari, A. H., Jantunen, E., Kauppila, M., Koistinen, P., Parto, K., Riikonen, P. V., et al. (2008) · Haematologica · doi:10.3324/haematol.12466 · Cited by 95
Mengozzi, M., Cervellini, I., Villa, P., Erbayraktar, Z., Gökmen, N., Yılmaz, O., Erbayraktar, S., Manohasandra, M., Hummelen, P. V., Vandenabeele, P., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1200554109 · Cited by 94
Moehring, A. J., Teeter, K. C., & Noor, M. A. F. (2006) · Molecular Biology and Evolution · doi:10.1093/molbev/msl142 · Cited by 94
Gozal, Y. M., Duong, D. M., Gearing, M., Cheng, D., Hanfelt, J., Funderburk, C. D., Peng, J., Lah, J. J., & Levey, A. I. (2009) · Journal of Proteome Research · doi:10.1021/pr900474t · Cited by 92
[2] Analyzing Micro‐RNA Expression Using Microarrays
Davison, T. S., Johnson, C. D., & Andruss, B. F. (2006) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(06)11002-2 · Cited by 91
MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data
Kaever, A., Landesfeind, M., Feussner, K., Mosblech, A., Heilmann, I., Morgenstern, B., Feußner, I., & Meinicke, P. (2014) · Metabolomics · doi:10.1007/s11306-014-0734-y · Cited by 94
CGHregions: dimension reduction for array CGH data with minimal information loss.
van de Wiel, M. A., & van Wieringen, W. N. (2007) · PubMed Central · Cited by 91
Functional Interpretation of Microarray Experiments
Dopazo, J. (2006) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2006.10.398 · Cited by 90
Abcouwer, S. F., Lin, C.-M., Wolpert, E. B., Shanmugam, S., Schaefer, É., Freeman, W. M., Barber, A. J., & Antonetti, D. A. (2010) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.10-5264 · Cited by 90
Caimari, A., Oliver, P., Keijer, J., & Palou, A. (2010) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2009.0092 · Cited by 90
Romero, R. J., Erez, O., Maymon, E., Chaemsaithong, P., Xu, Z., Pacora, P., Chaiworapongsa, T., Done, B., Hassan, S. S., & Tarca, A. L. (2017) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2017.02.037 · Cited by 89
Rai, M. F., Tycksen, E., Sandell, L. J., & Brophy, R. H. (2017) · Journal of Orthopaedic Research® · doi:10.1002/jor.23661 · Cited by 88
Role of miR-19b and its target mRNAs in 5-fluorouracil resistance in colon cancer cells
Kurokawa, K., Tanahashi, T., Iima, T., Yamamoto, Y., Akaike, Y., Nishida, K., Masuda, K., Kuwano, Y., Murakami, Y., Fukushima, M., et al. (2012) · Journal of Gastroenterology · doi:10.1007/s00535-012-0547-6 · Cited by 91
DNA microarray analyses of melanoma gene expression: a decade in the mines
Hoek, K. S. (2007) · Pigment Cell Research · doi:10.1111/j.1600-0749.2007.00412.x · Cited by 88
Fernández, P. D. C., Rienzo, J. D., Fernández, L. G., Hopp, H. E., Paniego, N. B., & Heinz, R. A. (2008) · BMC Plant Biology · doi:10.1186/1471-2229-8-11 · Cited by 86
Visualization-based cancer microarray data classification analysis
Mramor, M., Leban, G., Demšar, J., & Zupan, B. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm312 · Cited by 84
Wang, Y., Carrie, C., Giraud, E., Elhafez, D., Narsai, R., Duncan, O., Whelan, J., & Murcha, M. W. (2012) · The Plant Cell · doi:10.1105/tpc.112.098731 · Cited by 85
A distribution free summarization method for Affymetrix GeneChip® arrays
Chen, Z., McGee, M., Liu, Q., & Scheuermann, R. H. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl609 · Cited by 85
Blood transcriptomics and metabolomics for personalized medicine
Li, S., Todor, A., & Luo, R. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2015.10.005 · Cited by 86
Das, P. K., Roychowdhury, A., Das, S., Roychoudhury, S., & Tripathy, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00247 · Cited by 85
Li, H., Yu, B., Li, J., Su, L., Yan, M., Zhang, J., Li, C., Zhu, Z., & Liu, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125013 · Cited by 84
Placental transcriptome in development and pathology: expression, function, and methods of analysis
Cox, B., Leavey, K., Nosi, U., Wong, F., & Kingdom, J. C. (2015) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2015.07.046 · Cited by 86
Kühn, K., Yin, G., Duncan, O., Law, S. R., Kubiszewski-Jakubiak, S., Kaur, P., Meyer, E. H., Wang, Y., Francs‐Small, C. C. D., Giraud, E., et al. (2014) · PLANT PHYSIOLOGY · doi:10.1104/pp.114.249946 · Cited by 83
Predicting cancer involvement of genes from heterogeneous data
Aragüés, R., Sander, C., & Oliva, B. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-172 · Cited by 80
Elyasigomari, V., Lee, D. A., Screen, H. R. C., & Shaheed, M. H. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.01.016 · Cited by 84
CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
van de Wiel, M. A., & van Wieringen, W. N. (2007) · Cancer Informatics · doi:10.1177/117693510700300031 · Cited by 81
Microarray-based Analysis of Ventilator-induced Lung Injury
Wurfel, M. M. (2007) · Proceedings of the American Thoracic Society · doi:10.1513/pats.200608-149jg · Cited by 80
Molecular ecology of marine turtles: New approaches and future directions
Lee, P. L. M. (2008) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2007.12.021 · Cited by 85
Gu, Z., Liu, J., Cao, K., Zhang, J., & Wang, J. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-56 · Cited by 79
Patient-oriented gene set analysis for cancer mutation data
Boca, S. M., Kinzler, K. W., Velculescu, V. E., Vogelstein, B., & Parmigiani, G. (2010) · Genome biology · doi:10.1186/gb-2010-11-11-r112 · Cited by 77
Methods for detecting DNA methylation in tumors: From bench to bedside
Shames, D. S., Minna, J. D., & Gazdar, A. F. (2006) · Cancer Letters · doi:10.1016/j.canlet.2006.10.014 · Cited by 78
Gene-Expression Signatures Can Distinguish Gastric Cancer Grades and Stages
Cui, J., Fan, L., Wang, G., Fang, X., Puett, J. D., & Xu, Y. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017819 · Cited by 78
Delivery of bortezomib with nanoparticles for basal-like triple-negative breast cancer therapy
Shen, S., Du, X., Liu, J., Sun, R., Zhu, Y., & Wang, J. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2014.12.043 · Cited by 79
Nettleton, D. S., Recknor, J. C., & Reecy, J. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm583 · Cited by 75
Comparison of small n statistical tests of differential expression applied to microarrays
Murie, C., Woody, O. Z., Lee, A. Y., & Nadon, R. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-45 · Cited by 84
Probe Selection and Expression Index Computation of Affymetrix Exon Arrays
Xing, Y., Kapur, K., & Wong, W. H. (2006) · PLoS ONE · doi:10.1371/journal.pone.0000088 · Cited by 78
How to deal with multiple endpoints in clinical trials
Neuhäuser, M. (2006) · Fundamental and Clinical Pharmacology · doi:10.1111/j.1472-8206.2006.00437.x · Cited by 77
Babelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments
Al‐Shahrour, F., Carbonell‐Caballero, J., Mínguez, P., Götz, S., Conesa, A., Tárraga, J., Medina, I., Alloza, E., Montaner, D. A., & Dopazo, J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn318 · Cited by 77
Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
Huttenhower, C., Flamholz, A. I., Landis, J. N., Sahi, S., Myers, C. L., Olszewski, K. L., Hibbs, M., Siemers, N. O., Troyanskaya, O. G., & Coller, H. A. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-250 · Cited by 77
The liver pharmacological and xenobiotic gene response repertoire
Natsoulis, G., Pearson, C. I., Gollub, J., Eynon, B. P., Ferng, J., Nair, R. V., Idury, R., Lee, M. D., Fielden, M. R., Brennan, R., et al. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.9 · Cited by 77
Zhao, B., Erwin, A., & Xue, B. (2017) · Genomics · doi:10.1016/j.ygeno.2017.08.007 · Cited by 78
Antenatal Dexamethasone Treatment Leads to Changes in Gene Expression in a Murine Late Placenta
Baisden, B. L., Sonne, S., Joshi, R., Ganapathy, V., & Shekhawat, P. (2007) · Placenta · doi:10.1016/j.placenta.2007.04.002 · Cited by 76
Alsina, L., Israelsson, E., Altman, M. C., Dang, K. K., Ghandil, P., Israël, L., von Bernuth, H., Baldwin, N. E., Qin, H., Jin, Z., et al. (2014) · Nature Immunology · doi:10.1038/ni.3028 · Cited by 75
Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data
Becker, N., Toedt, G., Lichter, P., & Benner, A. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-138 · Cited by 76
Hu, J., Qian, J., Borisov, O., Pan, S., Li, Y., Liu, T., Deng, L., Wannemacher, K. M., Kurnellas, M., Patterson, C. M., et al. (2006) · PROTEOMICS · doi:10.1002/pmic.200600026 · Cited by 76
Towards knowledge-based gene expression data mining
Bellazzi, R., & Zupan, B. (2007) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2007.06.005 · Cited by 75
Response diversity of Arabidopsis thaliana ecotypes in elevated [CO2] in the field
Li, P., Sioson, A. A., Mane, S. P., ULANOV, A. V., Grothaus, G., Heath, L. S., Murali, T. M., Bohnert, H. J., & Grene, R. (2006) · Plant Molecular Biology · doi:10.1007/s11103-006-9041-y · Cited by 75
Metadata
- Authors
- Allison, D. B., Cui, X., Page, G. P., & Sabripour, M.
- Affiliations
- University of Alabama (US)
- Corresponding author
- Allison, David B.
- Venue
- Nature Reviews Genetics
- Published
- 2006-01-01
- Volume
- 7
- Issue
- 1
- Pages
- 55-65
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2021129827
- Semantic Scholar
- 7aeace2306487c5e5f3ff6711e1a5d3fc1e4c899
- Cited by
- 1,339
- References
- 99
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 10 · 2023: 13 · 2022: 19 · 2021: 23 · 2020: 35 · 2019: 35 · 2018: 43 · 2017: 56 · 2016: 48 · 2015: 68 · 2014: 73 · 2013: 69 · 2012: 104
- FWCI
- 9.79
- Type
- journal article
- Language
- en
- Update notices
- erratum (2006-05-01)
- Open access
- closed
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- U.S. Department of Defense
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- microarray data analysis, gene expression analysis, statistical analysis, normalization, differential gene expression, consensus methods, reproducibility
- MeSH terms
- Algorithms, Computer Simulation, Data Interpretation, Statistical, Gene Expression Regulation, Genetic Techniques, Genetics, Humans, Models, Biological, RNA, Messenger, Models, Statistical, Cluster Analysis, DNA, Complementary, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Microarray Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review