Improved Biclustering Algorithm Based on Weighted Mean Square Residual
LIU Wenhua, LIANG Yongquan, FENG Zheng
College of Information Science and Engineering, Shandong University of Science and Technology,Qingdao 266590 Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao 266590
Abstract Existing biclustering algorithms can hardly discover biclusters with overlapping structures. Consequently, the correct bicluster structures hidden in gene expression data can not be effectively found. Moreover, the influence of the importance of the different conditions on the bicustering result is not taken into account in the process of adding and deleting conditions. An improved biclustering algorithm based on weighted mean square residual(IBWMSR) is proposed to overcome the above defects. The gene sets are firstly partitioned into initial biclusters by using fuzzy partition and the fuzzy partition is controlled by overlapping ratio and the membership of the genes. Then, the weights of the conditions in each bicluster are iteratively updated in the process of minimizing the objective function. Finally, the bicluster set is obtained after adding the genes satisfying the constraints and removing the genes producing inconsistency fluctuation. Theexperiment shows that the proposed algorithm generates the biclusters with similar expression level of different sizes and restricts the overlapping ratio to a reasonable range.
Corresponding Authors:
LIANG Yongquan(Corresponding author), born in 1967, Ph.D., professor. His research interests include artificial inte-lligence and intelligent information processing.
About author:: LIU Wenhua, born in 1990, master student. Her research interests include machine learning and data mining.FENG Zheng, born in 1988, master student. His research interests include machine learning and data mining.
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2016, Vol. 29 
