The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The Gene Expression Omnibus Database
Open accessAbstract
The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome–protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/.
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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
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Initial sequencing and comparative analysis of the mouse genome
Mouse Genome Sequencing Consortium (2002) · Nature · doi:10.1038/nature01262 · Cited by 7,187
Mutations in ABC1 in Tangier disease and familial high-density lipoprotein deficiency
Brooks-Wilson, A., Marcil, M., Clee, S. M., Zhang, L.-H., Roomp, K., van Dam, M., Yu, L., Brewer, C., Collins, J. A., Molhuizen, H. O. F., et al. (1999) · Nature Genetics · doi:10.1038/11905 · Cited by 1,687
Chen, W., Stambolian, D., Edwards, A. O., Branham, K. E., Othman, M., Jakobsdottir, J., Tosakulwong, N., Pericak-Vance, M. A., Campochiaro, P. A., Klein, M. L., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912702107 · Cited by 506
Cited by
The Application of Deep Learning in Cancer Prognosis Prediction
Zhu, W., Xie, L., Han, J., & Guo, X. (2020) · Cancers · doi:10.3390/cancers12030603 · Cited by 380
Predicting perturbation patterns from the topology of biological networks
Santolini, M., & Barabási, A.-L. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720589115 · Cited by 323
Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients
(DeCOI), G. C.-1. O. I., Aschenbrenner, A. C., Mouktaroudi, M., Krämer, B., Oestreich, M., Antonakos, N., Nuesch-Germano, M., Gkizeli, K., Bonaguro, L., Reusch, N., et al. (2021) · Genome Medicine · doi:10.1186/s13073-020-00823-5 · Cited by 255
Liu, L., Xie, Y., Yang, H., Lin, A., Dong, M., Wang, H., Zhang, C., Liu, Z., Cheng, Q., Zhang, J., et al. (2023) · iMeta · doi:10.1002/imt2.130 · Cited by 62
Helmy, M., Elhalis, H., Liu, Y., Chow, Y., & Selvarajoo, K. (2023) · Advances in Nutrition · doi:10.1016/j.advnut.2022.11.002 · Cited by 57
Metadata
- Authors
- Clough, E., & Barrett, T.
- Venue
- Methods in Molecular Biology
- Publisher
- Springer New York
- Published
- 2016
- Volume
- 1418
- Pages
- 93-110
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- OpenAlex
- W2341128841
- Cited by
- 2,459
- References
- 6
- FWCI
- 93.79
- Type
- book chapter
- Language
- en
- Open access
- green
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