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Mfuzz: A software package for soft clustering of microarray data
Open accessAbstract
UNLABELLED: For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constructed an R package termed Mfuzz implementing soft clustering tools for microarray data analysis. The additional package Mfuzzgui provides a convenient TclTk based graphical user interface. AVAILABILITY: The R package Mfuzz and Mfuzzgui are available at http://itb1.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/index.html. Their distribution is subject to GPL version 2 license.
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Cited by
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Cardoso-Moreira, M., Halbert, J., Valloton, D., Velten, B., Chen, C., Shao, Y., Liechti, A., Ascenção, K., Rummel, C., Ovchinnikova, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1338-5 · Cited by 838
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Rigbolt, K. T. G., Prokhorova, T. A., Akimov, V., Henningsen, J., Johansen, P. T., Kratchmarova, I., Kassem, M., Mann, M., Olsen, J. V., & Blagoev, B. (2011) · Science Signaling · doi:10.1126/scisignal.2001570 · Cited by 451
Mertins, P., Yang, F., Liu, T., Mani, D. R., Petyuk, V. A., Gillette, M. A., Clauser, K. R., Qiao, J. W., Gritsenko, M. A., Moore, R. J., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.036392 · Cited by 400
Metadata
- Authors
- Kumar, L., & Futschik, M. E.
- Venue
- Bioinformation
- Publisher
- Biomedical Informatics
- Published
- 2007-05-20
- Volume
- 2
- Issue
- 1
- Pages
- 5-7
- ISSN
- 0973-2063 (ISSN-L)
- 0973-8894
- OpenAlex
- W2050040190
- Cited by
- 1,986
- References
- 0
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
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