- Title
- FusGP: Bayesian co-learning of gene regulatory networks and protein interaction networks
- Creator
- Morshed, Nizamul; Chetty, Madhu; Nguyen, Vinh
- Date
- 2012
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/157187
- Identifier
- vital:11548
- Identifier
-
https://doi.org/10.1007/978-3-642-34500-5_44
- Abstract
- Understanding gene interactions is a fundamental question in uncovering the underlying biological relations that enable successful functioning of living organisms. The modeling of gene regulations is usually done using DNA microarray data. However, presence of noise and the scarcity of microarray data affect the reconstruction of gene regulatory networks. In this paper, we propose a novel co-learning based fusion algorithm using the dynamic Bayesian netowrk (DBN) formalism for reconstruction of gene regulatory networks which incorporates knowledge obtained from protein-protein interaction networks to improve network accuracy. The proposed approach is efficient and naturally amenable to parallel computation. We apply the algorithm on the well-known Saccharomyces cerevisiae gene expression data that shows the effectiveness of our approach.
- Publisher
- Springer
- Relation
- 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012; published in Neural Information Processing, Part V (Lecture Notesin Computer Science series) Vol.7667 p. 369-377
- Rights
- Copyright Springer
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bayesian network; Gene regulatory network; Binary Markov network; Protein interaction network
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