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ProBic: identification of overlapping biclusters using Probabilistic Relational Models, applied to simulated gene expression data.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bulcke, Tim Van Den Zhao, H. Engelen, Kristof Moor, Bart De Marchal, Kathleen |
| Copyright Year | 2001 |
| Abstract | We propose a novel PRM based biclustering model, in which gene expression data can be considered as relational data. The classes are Gene, Condition and Expression. Both the classes Gene and Condition have a vector attribute Bicluster containing a series of bicluster-id’s. These vectors represent which biclusters exist for a gene or condition and are initially unknown. Condition has an extra attribute ID, which is a unique number for each condition. Expression has an attribute Level containing the expression value and two reference slots which point to the gene and condition for which the level was measured. Expression.Level is conditionally dependent on Gene.Bicluster, Condition.Bicluster and Condition.ID. The conditional dependency is modeled as a set of Gaussian distributions with conjugate priors. The ProBic model naturally deals with missing values (in fact, there are no ‘missing’ values in this model) and robust sets of biclusters are obtained due to explicit modeling of noise. The maximum likelihood solution is approximated using an Expectation-Maximization strategy. ProBic was applied to simulated gene expression data sets and all the biclusters were successfully identified. Various noise settings and different overlap models (average, sum, product) have been explored. Our results show that PRM models can be used to identify overlapping biclusters in an efficient and robust manner, naturally dealing with missing values and noise. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ml.dcs.shef.ac.uk/pmnp/abstracts/Bulcke.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
