Conference proceeding
Weighted Consensus Clustering for Identifying Functional Modules in Protein-Protein Interaction Networks
2009 International Conference on Machine Learning and Applications, pp.539-544
12/2009
DOI: 10.1109/ICMLA.2009.94
Abstract
In this article we present a new approach - weighted consensus clustering to identify the clusters in Protein-protein interaction (PPI) networks where each cluster corresponds to a group of functionally similar proteins. In weighed consensus clustering, different input clustering results weigh differently, i.e., a weight for each input clustering is introduced and the weights are automatically determined by an optimization process. We evaluate our proposed method with standard measures such as modularity, normalized mutual information (NMI) and the Gene Ontology (GO) consortium database and compare the performance of our approach with other consensus clustering methods. Experimental results demonstrate the effectiveness of our proposed approach.
Details
- Title: Subtitle
- Weighted Consensus Clustering for Identifying Functional Modules in Protein-Protein Interaction Networks
- Creators
- Erliang Zeng - Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, WA, AustraliaY Zhang - Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USATao Li - Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USAG Narasimhan - Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2009 International Conference on Machine Learning and Applications, pp.539-544
- DOI
- 10.1109/ICMLA.2009.94
- Publisher
- IEEE
- Language
- English
- Date published
- 12/2009
- Academic Unit
- Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute; Biostatistics; Dental Research
- Record Identifier
- 9984065471402771
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