Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Linear Modeling of Genetic Networks from Experimental Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Data analysis with bayesian networks: a bootstrap approach
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Journal of Biomedical Informatics
Machine Learning
Stability analysis of uncertain genetic sum regulatory networks
Automatica (Journal of IFAC)
Constructing explanatory process models from biological data and knowledge
Artificial Intelligence in Medicine
Evolutionary search for improved path diagrams
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Analyzing pathways using ASP-based approaches
ANB'10 Proceedings of the 4th international conference on Algebraic and Numeric Biology
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Discovering the complex regulatory networks that govern mRNA expression is an important but difficult problem. Many current approaches use only expression data from microarrays to infer the likely network structure. However, this ignores much existing knowledge because for a given organism and system under study, a biologist may already have a partial model of gene regulation. We propose a method for revising and improving these initial models, which may be incomplete or partially incorrect, with expression data. We demonstrate our approach by revising a model of photosynthesis regulation proposed by a biologist for Cyanobacteria. Applied to wild type expression data, our system suggested several modifications consistent with biological knowledge. Applied to a mutant strain, our system correctly modified the disabled gene. Power experiments with synthetic data that indicate that reliable revision is feasible even with a small number of samples.