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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Min Li Xuehong Wu Jianxin Wang Yi Pan |
| Copyright Year | 2011 |
| Abstract | Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes and functional modules, which is crucial to understanding the principles of cellular organization and prediction of protein functions. In the past few years, many computational methods have been proposed. However, it is always a challenging task to evaluate how well the clusters are identified. Even for the most popular measurements, F-measure and Pvalue, bias exists for evaluating the identified clusters. In this paper, we propose a new measurement, named hF-measure, to evaluate clusters more finely and distinctly. First, we defined the hierarchical consistency and the hierarchical similarity. Then, we propose a new hierarchical measurement of hF-measure by taking into account the hierarchical organization of functional annotations and the functional similarities among proteins. The new measurement hF-measure can discriminate between different types of errors which cannot be distinguished by F-measure. The experimental results based on Gene Ontology (GO) and yeast functional modules show that hF-measure evaluates clusters more accurately when compared to F-measure. |
| Starting Page | 63 |
| Ending Page | 68 |
| File Size | 316573 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457717994 |
| DOI | 10.1109/BIBM.2011.47 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins evaluation Protein engineering Protein-protein interaction network Measurement uncertainty Hafnium Organizations clustering Biomedical measurements algorithm |
| Content Type | Text |
| Resource Type | Article |
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