Inferring biomolecular interaction networks based on convex optimization
Computational Biology and Chemistry
A Partial Granger Causality Approach to Explore Causal Networks Derived From Multi-parameter Data
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
An improved shrinkage estimator to infer regulatory networks with Gaussian graphical models
Proceedings of the 2009 ACM symposium on Applied Computing
Reverse engineering of gene regulatory networks: a comparative study
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
Learning Gaussian graphical models of gene networks with false discovery rate control
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
A hyper-graph approach for analyzing transcriptional networks in breast cancer
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Crosstalk measures for analyzing biological networks in breast cancer
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Learning Bayesian networks with integration of indirect prior knowledge
International Journal of Data Mining and Bioinformatics
Bayesian network structure inference with an hierarchical Bayesian model
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Non-stationary bayesian networks based on perfect simulation
Proceedings of the 21st ACM international conference on Information and knowledge management
Hi-index | 3.84 |
Motivation: An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions. Results: On Gaussian observational data, BNs and GGMs were found to outperform RNs. The difference in performance was not significant for the non-linear simulated data and the cytoflow data, though. Also, we did not observe a significant difference between BNs and GGMs on observational data in general. However, for interventional data, BNs outperform GGMs and RNs, especially when taking the edge directions rather than just the skeletons of the graphs into account. This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs. Availability: Data, software and supplementary material are available from http://www.bioss.sari.ac.uk/staff/adriano/research.html. Contact:adriano@bioss.ac.uk, dirk@bioss.ac.uk, Grzegorc@statistik.uni-dortmund.de