Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Protein Secondary-Structure Modeling with Probabilistic Networks
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
COPASI---a COmplex PAthway SImulator
Bioinformatics
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
The factor graph network model for biological systems
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Component-based construction of bio-pathway models: The parameter estimation problem
Theoretical Computer Science
A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology Pathway model construction is often an inherently incremental process, with new pathway players and interactions continuously being discovered and additional experimental data being generated Here we focus on the problem of performing model parameter estimation incrementally by integrating new experimental data into an existing model A probabilistic graphical model known as the factor graph is used to represent pathway parameter estimates By exploiting the network structure of a pathway, a factor graph compactly encodes many parameter estimates of varying quality as a probability distribution When new data arrives, the parameter estimates are refined efficiently by applying a probabilistic inference algorithm known as belief propagation to the factor graph A key advantage of our approach is that the factor graph model contains enough information about the old data, and uses only new data to refine the parameter estimates without requiring explicit access to the old data To test this approach, we applied it to the Akt-MAPK pathways, which regulate the apoptotic process and are among the most actively studied signaling pathways The results show that our new approach can obtain parameter estimates that fit the data well and refine them incrementally when new data arrives.