@inproceedings{553404b49919499a9c53d49daea86bc1,
title = "MFCluster: Mining maximal fault-tolerant constant row biclusters in microarray dataset",
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.",
keywords = "bicluster, constant row, fault-tolerant, microarray",
author = "Miao Wang and Xuequn Shang and Miao Miao and Zhanhuai Li and Wenbin Liu",
year = "2011",
doi = "10.1007/978-3-642-23535-1_17",
language = "英语",
isbn = "9783642235344",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "181--190",
booktitle = "Web-Age Information Management - 12th International Conference,WAIM 2011, Proceedings",
note = "12th International Conference on Web-Age Information Management, WAIM 2011 ; Conference date: 14-09-2011 Through 16-09-2011",
}