Abstract
Biclustering is one of the most popular methods for microarray dataset analysis, which allows for conditions and genes clustering simultaneously. However, due to the influence of experimental noise in the microarray dataset, using traditional biclustering methods may neglect some significative biological biclusters. In order to reduce the influence of noise and find more types of biological biclusters, we propose an algorithm, MFCluster, to mine fault-tolerant biclusters in microarray dataset. MFCluster uses several novel techniques to generate fault-tolerant efficiently by merging non-relaxed biclusters. MFCluster generates a weighted undirected relational graph firstly. Then all the maximal fault-tolerant biclusters would be mined by using pattern-growth method in above graph. The experimental results show our algorithm is more efficiently than traditional ones.
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Wang, M., Shang, X., Miao, M., Li, Z., Liu, W. (2011). MFCluster: Mining Maximal Fault-Tolerant Constant Row Biclusters in Microarray Dataset. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23535-1_17
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DOI: https://doi.org/10.1007/978-3-642-23535-1_17
Publisher Name: Springer, Berlin, Heidelberg
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