期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 43, 期 11, 页码 1965-1977出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2013.07.021
关键词
Microarray data; Gene expression data clustering; Clustering; Multiobjective optimization (MOO); Symmetry; Archived multiobjective simulated annealing based technique (AMOSA); Automatic determination of number of clusters
The invention of microarrays has rapidly changed the state of biological and biomedical research. Clustering algorithms play an important role in clustering microarray data sets where identifying groups of co-expressed genes are a very difficult task. Here we have posed the problem of clustering the microarray data as a multiobjective clustering problem. A new symmetry based fuzzy clustering technique is developed to solve this problem. The effectiveness of the proposed technique is demonstrated on five publicly available benchmark data sets. Results are compared with some widely used microarray clustering techniques. Statistical and biological significance tests have also been carried out. (C) 2013 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据