Conference proceeding
Combining Gene Expression Profiles and Protein-Protein Interactions for Identifying Functional Modules
2012 11th International Conference on Machine Learning and Applications, Vol.1, pp.114-119
12/2012
DOI: 10.1109/ICMLA.2012.28
Abstract
Identifying functional modules from protein-protein interaction networks is an important and challenging task. This paper presents a new approach called PPIBM which is designed to integrate gene expression data analysis and clustering of protein-protein interactions. The proposed approach relies on a Bayesian model which uses as its base protein-protein interactions given as part of input. The proposed method is evaluated with standard measures and its performance is compared with the state-of-the-art network analysis methods. Experimental results on both real-world data and synthetic data demonstrate the effectiveness of the proposed approach.
Details
- Title: Subtitle
- Combining Gene Expression Profiles and Protein-Protein Interactions for Identifying Functional Modules
- Creators
- Dingding Wang - Center for Comput. Sci., Univ. of Miami, Coral Gables, FL, USAM Ogihara - Center for Comput. Sci., Univ. of Miami, Coral Gables, FL, USAErliang Zeng - Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USATao Li - Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2012 11th International Conference on Machine Learning and Applications, Vol.1, pp.114-119
- DOI
- 10.1109/ICMLA.2012.28
- Publisher
- IEEE
- Language
- English
- Date published
- 12/2012
- Academic Unit
- Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute; Biostatistics; Dental Research
- Record Identifier
- 9984065471002771
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