An effective non-parametric method for globally clustering genes from expression profiles
- Details
- Publication Year 2007-12,Volume 45,Issue #12,Page 1175-1185
- Journal Title
- MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Publication Type
- Journal Article
- Abstract
- Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm.
- Publisher
- SPRINGER HEIDELBERG
- Keywords
- MICROARRAY DATA
- Publisher's Version
- https://doi.org/10.1007/s11517-007-0271-1
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2007-12-01 12:00:00