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Natural Computing: an international journal
New probabilistic graphical models for genetic regulatory networks studies
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A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
The Journal of Machine Learning Research
EURASIP Journal on Bioinformatics and Systems Biology
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
Biological network inference using redundancy analysis
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
A framework for path analysis in gene regulatory networks
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Parallel information theory based construction of gene regulatory networks
HiPC'08 Proceedings of the 15th international conference on High performance computing
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Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Markov-Blanket-Based Model for Gene Regulatory Network Inference
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A hybrid algorithm to infer genetic networks
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Reverse engineering of gene regulatory networks from biological data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Learning undirected graphical models from multiple datasets with the generalized non-rejection rate
International Journal of Approximate Reasoning
An analysis of correlative and static causality in p systems
CMC'12 Proceedings of the 13th international conference on Membrane Computing
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
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Motivation: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. Results: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. Availability: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software Supplementary information: Supplementary information can be found at: URL to be decided