ArticleMedical & biological engineering & computing2007
An effective non-parametric method for globally clustering genes from expression profiles.
Article in Medical & biological engineering & computing, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou's PseAAC.Medical & biological engineering & computing · 2015Article
- The Nightingale Prize for best MBEC paper in 2011.Medical & biological engineering & computing · 2012Article
- The Nightingale Prize 2007.Medical & biological engineering & computing · 2008Article
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Authors and funding
4 authors.
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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.
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