Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Monte Carlo methods
Learning in graphical models
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Modeling and analysis of heterogeneous regulation in biological networks
RRG'04 Proceedings of the 2004 RECOMB international conference on Regulatory Genomics
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Incremental signaling pathway modeling by data integration
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Composing globally consistent pathway parameter estimates through belief propagation
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
| Hi-index | 0.00 |
We introduce an extended computational framework for studying biological systems. Our approach combines formalization of existing qualitative models that are in wide but informal use today, with probabilistic modeling and integration of high throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph and the framework accommodates partial measurements of diverse biological elements. We develop methods for inference and learning in the model. We compare the performance of standard inference algorithms and tailor-made ones and show that hidden variables can be reliably inferred even in the presence of feedback loops and complex logic. We develop a formulation for the learning problem in our model which is based on deterministic hypothesis testing, and show how to derive p-values for learned model features. We test our methodology and algorithms on both simulated and real yeast data. In particular, we use our method to study the response of S. cerevisiae to hyper-osmotic shock, and explore uncharacterized logical relations between important regulators in the system.