The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Gene expression data analysis
Open accessAbstract
Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices – tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification. Then we discuss how the gene expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.
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Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Gene expression profile of aging and its retardation by caloric restriction
Lee, C.-K., Klopp, R. G., Weindruch, R., & Prolla, T. A. (1999) · Science · doi:10.1126/science.285.5432.1390 · Cited by 1,473
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A N EW A PPROACH TO D ECODING L IFE : Systems Biology
Ideker, T., Galitski, T., & Hood, L. (2001) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev.genom.2.1.343 · Cited by 1,664
The mutual information: detecting and evaluating dependencies between variables
Steuer, R., Kurths, J., Daub, C. O., Weise, J., & Selbig, J. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_2.s231 · Cited by 815
Iizuka, N., Oka, M., Yamada-Okabe, H., Nishida, M., Maeda, Y., Mori, N., Takao, T., Tamesa, T., Tangoku, A., Tabuchi, H., et al. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12775-4 · Cited by 511
Applications of transformer-based language models in bioinformatics: a survey
Zhang, S., Fan, R., Liu, Y., Chen, S., Liu, Q., & Zeng, W. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad001 · Cited by 176
Metadata
- Authors
- Brazma, A., & Vilo, J.
- Venue
- FEBS Letters
- Publisher
- Wiley
- Published
- 2000-08-24
- Volume
- 480
- Issue
- 1
- Pages
- 17-24
- ISSN
- 0014-5793 (ISSN-L)
- 1873-3468
- OpenAlex
- W2060406328
- Cited by
- 598
- References
- 2
- FWCI
- 18.36
- Type
- journal article
- Language
- en
- Open access
- bronze
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