Module networks revisited: computational assessment and prioritization of model predictions
| dc.creator | Joshi, Anagha | |
| dc.creator | De Smet, Riet | |
| dc.creator | Marchal, Kathleen | |
| dc.creator | Van de Peer, Yves | |
| dc.creator | Michoel, Tom | |
| dc.date | 2009-01-12 | |
| dc.date.accessioned | 2026-07-07T12:28:24Z | |
| dc.date.available | 2026-07-07T12:28:24Z | |
| dc.description | The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. We revisit the approach of Segal et al. (2003) to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging. | |
| dc.description | 8 pages REVTeX, 6 figures | |
| dc.identifier | https://arxiv.org/abs/0901.1544 | |
| dc.identifier | http://arxiv.org/abs/0901.1544 | |
| dc.identifier | doi:10.1093/bioinformatics/btn658 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/215528 | |
| dc.subject | Quantitative Methods | |
| dc.subject | Molecular Networks | |
| dc.title | Module networks revisited: computational assessment and prioritization of model predictions | |
| dc.type | text |